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This special issue is dedicated to Prof. Gottfried Konecny (see Figure 1), the former president of the International Society for Photogrammetry and Remote Sensing (ISPRS) on the occasion of his 90t...

Remote sensing mapping is an important research direction in the development of geographic surveying and mapping. In order to successfully implement the project of Mapping Western China (MWC), a technical mapping system has been established. In this project, many problems have been solved through technological innovation, such as block adjustment with scarce control points, large-scale aerial/satellite image mapping, and intelligent interpretation of multi-source images. Several softwares were developed, e.g. PixelGrid for aerial/satellite image mapping in a large area, FeatureStation for the integration of multi-source data in the complex terrain areas, and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR mapping. For the first time, full coverage of 1: 50,000 topographic data of China’s land territory has been produced, which means the geospatial framework of digital China is basically completed. With the implementation of other key national plans and projects (i.e. national geographic conditions monitoring and national remote sensing mapping), the focus has changed from MWC to national dynamic mapping. Accordingly, a dynamic mapping system is established. The data acquisition capability has developed from a single source to multiple sources and multiple modalities. The mapping capability has developed into dynamic mapping, and the capability for database update shows the characteristics of collaboration. The national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs. A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information service. In this paper, we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition, map production, and information service. The purpose of this paper is to motivate the establishment of theory and method for remote sensing mapping, technical and equipment in the smart mapping era, to improve the capability of perceiving, analyzing, mining, and applying geographic data, and to promote the intelligent development of geographic surveying and mapping.

A 90th birthday would be a milestone celebration for any person, but exceptionally rare if the person is still active after 75 years of working and serving the photogrammetric and remote sensing communities worldwide. This unprecedented career is the motivation to reflect on the technological developments of the past 75 years in the light of Gottfried Konecny’s lifetime achievements. I started to get to know Gottfried not long before he officially retired from the Institute for Photogrammetry and Engineering Surveys in 1998. Furthermore, I was not even born when he, as a young and talented scholar supported by the Fulbright Scholarship from the US Government, arrived at the Ohio State University (OSU), which has been my professional home for three decades. In the second half of Gottfried’s 75-year career, the technological changes have been phenomenal, and, furthermore, shown an accelerating trend in the past few years. It is absolutely remarkable how Gottfried has been able to keep up with these rapid changes and advancing technologies and exploits them for mapping and, in general, for all the users of the geospatial field at large. Additionally, his open-minded and kind personality has been always a major plus to his technical skills and experiences, and ultimately helped him assist many colleagues to advance their careers, and along the way developed and increased the use of geospatial data and systems in support of many countries worldwide.

The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna, Austria. Under this agreement panchromatic and multispectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research. So far, nearly 500 GB of data have been uploaded to the server. This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies. Some of them are already completed, others are still ongoing. They include a geometric accuracy validation of ZY-3 data, an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos, and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria. An accuracy study of DTMs from ZY-3 stereo data, as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

Urban surface morphology is an important key factor in determining the temporal variation of thermal anisotropy. This study uses drones based high-resolution Digital Surface Models (DSM) to explore the impact of morphological urban variability expressed by terrain factors such as diurnal anisotropy patterns, sky view factor, solar radiation and solar duration effects on the urban heating island. The goal of this study is to build a topo-climatologic map from the terrain factors and determine its effects on the Urban Heating Islands. a test area of 2 square km was selected, including open grasslands, forests, and built-up areas. Itinerant measurements are made using portable sensors to measure temperature and wind direction in the study area. the measurement points are chosen according to the result of the topographic map and the topo climatologic map. the measures taken allowed us to study the temperature of the region during the day and at night and to detect the urban heat island of the region., African Journal on Land Policy and Geospatial Sciences, Vol. 4, No 1: January 2021

The Unmanned Aerial vehicle (UAV), also known as drone, is a photogrammetric platform used as alternative solution to overcome the limits of traditional surveying methods. This study aims to analyze a small study area in Yaoundé, Cameroun using high resolution geospatial data of UAV and STDM for recording of land right in informal settlement. The study shows that, the high-resolution aerial images of UAV based on orthophotos can be used to solve problems related to land governance and the regularization of tenure rights through participatory boundary delineation and mapping approach. We noticed many problems related to the land ownership illustrated by contradictions between the municipal administration data and the results of our investigations. The CUY publishes that, out of 16.70% of the people with land certificates, 9% of the potential owners confirmed their rights, while the land map issued by the council is showing three. A well-established municipal housing estate would never have been transformed into an informal settlement with the existence of its own computerize land management system. The advantages of UAVs (large and rapid aerial georeferenced data collection in less time, with high accuracy at low cost) make it possible to develop such a system in order to secure the land rights of all, particularly for the poor living in informal settlements.

The paper contains a brief overview of the professional activities of Professor Gottfried Konecny, Professor Emeritus at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Germany, and his influence on photogrammetry, remote sensing and geospatial information science during his time at the University of New Brunswick, Canada, and at the Leibniz University. His work can be divided into educational, scientific, application, organizational, and work in international societies. Additional activities comprised of giving courses in various languages, consultancy activities, and various other cooperations which took place in many South American countries, in Africa and Asia, for organizations such as the World Bank, United Nations, German Space Agency DLR, to name only a few. Activities in connection with international societies mainly took place during Prof. Konecny’s engagement in the Council of the International Society for Photogrammetry (ISP), renamed the International Society for Photogrammetry and Remote Sensing (ISPRS), and the European Association of Remote Sensing Laboratories (EARSeL) Bureau. He was a key figure in bringing many countries and organizations to ISPRS as Ordinary and Sustaining Members. Professor Konecny has always been aware of the importance of international cooperation at various levels, also with sister societies such as the International Cartographic Association and the United Nations – Global Geospatial Information Management (UN-GGIM). His application activities span from the photogrammetry-oriented projects he was awarded in Canada, to remote sensing topics and geospatial sciences, combined with cartography during his Hannover period. His vision and endless effort are exemplary for a person who has always been devoted to the three geosciences mentioned above. Professor Konecny is the author of a number of books, journal papers and papers presented at international and national conferences. He has been a recognized keynote speaker at many conferences throughout the world due to his vast experience. This paper describes 75 years of a man who is an expert in fields which arose during his academic life.

In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper.

I am pleased to contribute an article to GSIS to commemorate the great achievements of Prof. Dr. Gottfried Konecny on the occasion of the celebration of his 90th birthday.I was lucky to work closel...

The COVID-19 (coronavirus) disease has affected over 100 countries in a matter of weeks. The way humans live and work on planet Earth has changed profoundly over the last months. Many lives and livelihoods have been lost. Ways of working, shopping, and social interactions are all vastly different for most of us. We do not know when the current crisis will end, or how far we will “return” to the way things were before the pandemic. The world is experiencing one of the most transformative moments of the last 100 years. The social, economic, and political influences of the COVID-19 crisis have already been hugely momentous, we do not utterly understand it. Looking ahead, we have inevitably had to make important public health, economic, governance, and ecological decisions with less information than usual and to reverse recently adopted policies, which can lead to an era of disruptive transformations to sustainability. During these historic times, we must understand the hidden reality of the earth system and attempt to transform our needs so that we all have a sustained future on our planet.

30-m Global Land Cover (GLC) data products permit the detection of land cover changes at the scale of most human land activities, and are therefore used as fundamental information for sustainable development, environmental change studies, and many other societal benefit areas. In the past few years, increasing efforts have been devoted to the accuracy assessment of GlobeLand30 and other finer-resolution GLC data products. However, most of them were conducted either within a limited percentage of map sheets selected from a global scale or in some individual countries (areas), and there are still many areas where the uncertainty of 30-m resolution GLC data products remains to be validated and documented. In order to promote a comprehensive and collaborative validation of 30-m GLC data products, the GEO Global Land Cover Community Activity had organized a project from 2015 to 2017, to examine and explore its major problems, including the lack of international agreed validation guidelines and on-line tools for facilitating collaborative validation activities. With the joint effort of experts and users from 30 GEO member countries or participating organizations, a technical specification for 30-m GLC validation was developed based on the findings and experiences. An on-line validation tool, GLCVal, was developed by integrating land cover validation procedures with the service computing technologies. About 20 countries (regions) have completed the accuracy assessment of GlobeLand30 for their territories with the guidance of the technical specification and the support of GLCVal.

Groundwater is an important natural resource for domestic, agricultural and industrial use. Today, due to climate change, population growth, advanced irrigation practices and industrial uses, the demand for groundwater has increased considerably, and needs to be assessed. This article aims to delimit the potential groundwater zones in the Sandougou basin using GIS and remote sensing. The multi-criteria decision analysis technique (MCDA) is used by integrating different thematic layers. The thematic layers of land cover, drainage density, linear density, soil, geology, geomorphology, terrain, slope and rain were prepared and used to make the map potential groundwater areas. Weights have been assigned and standardized to each thematic layer according to their characteristic and their relation to the groundwater recharge. Finally, the thematic maps were integrated by a tool for analyzing the weighted sum of overlays to develop the areas for prospecting for groundwater. According to this method, the basin is classified as excellent for 30.9% of the surface, very good for 27.8%, moderate for 20.5%, poor for 15.3% and very poor for 4.4% in term of groundwater potential. This analysis of the data demonstrates the importance of the use of GIS and remote sensing techniques in mapping the potential groundwater sector on a regional scale and suggests that similar techniques could be applied to other regions of this country., African Journal on Land Policy and Geospatial Sciences, Vol. 4, No 1: January 2021

Land administration as one of the mechanisms of availing property rights to owners cannot be achieved unless there are well-functioning and coordinated institutional frameworks. This paper explored the challenges emanating from the existing institutional frameworks dealing with land administration in providing property right in Burundi. A mixed research design has been applied to capture qualitative and quantitative data. A desk review of different government reports, academic papers and books have been consulted. A total of 60 respondents have participated in this paper such as officials in land department, local leaders, and individual land owners. The findings show that Burundi has got many institution frameworks, but are fragmented and dispersed. As results, challenges faced by many groups of people are not addressed by these institutions which make these people to have less property rights. These people are marginalised, suffering from food insecurity and living in extreme poverty., African Journal on Land Policy and Geospatial Sciences, Vol. 4, No 1: January 2021

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.).

Since the beginning of the twenty-first century, several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system. Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system, China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology. The development of domestic high-resolution remote sensing satellites shows a vigorous trend, and consequently, a relatively stable and perfect high-resolution earth observation system has been formed. The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods. In this paper, the de velopment status, along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed, and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed, providing a reference for the subsequent development of high-resolution remote sensing satellites in China.

Since 2011 an intensive international cooperation has been in place between the Institute for Applied Photogrammetry and Geoinformatics (IAPG) at the Jade University of Applied Sciences in Oldenburg, Germany, and various Ukrainian universities. Following an initial contact by Prof. Gottfried Konecny, the first visit was organized, and was followed by many more. In subsequent years an intensive cooperation was established particularly with the National University for Construction and Architecture (KNUCA) in Kiev. In addition to architects and civil engineers, KNUCA also trains geodesists, geo-information scientists and landscape planners. The cooperation today includes the reciprocal exchange of scientists and students, research projects, courses and cooperation at many other levels. In addition, a commercial company has been established, SPM3D LLC, which now employs more than 14 engineers in the field of 3D acquisition, point cloud processing and modeling. This article summarizes the history of the cooperation and presents the results of associated student projects. In addition, results of joint work on the development of a virtual laser scanner are presented, part of a German-Ukrainian initiative to digitize teaching.

Metro is an important form of public transport in Shanghai. Based on the metro card data, we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time. Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations. It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns. Based on various characteristics, 285 metro stations are classified into four types, including residential-oriented stations, employment-oriented stations, employment-residence-oriented stations, and integrated functional-oriented stations, reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.

Urban barriers are often a consequence of the unplanned expansion of transport infrastructures, housing growth nearby existing transport infrastructures, and the vehicular traffic itself. This process, also known as “community severance”, is a problem that affects the travel patterns of the population and causes negative impacts to adjacent communities, but it is not easy to assess. As an alternative to the costly technologies and restricted-access data sometimes used to analyze urban barriers, we propose an analytical approach that assesses community severance levels by classifying the quality of Pedestrian Crossings on Urban Streets (the PeCUS index). Additionally, the approach is used to identify possible inequities nearby the different classification groups regarding demographic data. We used the chi-square (χ2) test of independence and the standardized Pearson residuals to indicate deficits and excesses of residents regarding the demographic characteristics studied surrounding the urban streets. We found the following evidences of associations: residents with low-income or those aged up to 19 tend to live close to the worst crossings, whereas residents with permanent mobility constraints or the elderly tend to live near the crossings with the best classifications. Therefore, the study shows that the assessment of community severance by the analysis of the PeCUS index is feasible. Moreover, it reveals that the distribution of residents surrounding the distinct classes of quality of crossings is equitable for vulnerable social groups, except for low-income or young residents.

When arranging the pedestrian infrastructure, one of the most important components that make a tangible contribution to the safety of pedestrians is to organize the safe road crossing. In cities, pedestrians often cross a road in the wrong place due to established routes or inadequate location of crosswalks. Accidents with the participation of pedestrians who crossed the road neglecting the traffic rules, make up a significant part of the total amount of road accidents. In this paper, we propose a method that allows us, on the basis of the results of a computer simulation of pedestrian traffic, to obtain predicted routes for road crossing and to indicate optimal locations for crosswalks that take into account established pedestrian routes and increase their safety. The work describes an extension for the existing AntRoadPlanner simulation algorithm, which searches for and clusters points where pedestrians cross the roadway and suggests locations for new crosswalks. This method was tested on the basis of a comparative simulation of several territories before and after its application, as well as on the basis of a field study of the territories. The developed algorithm can also be used to search for other potentially dangerous places for pedestrians on plans of districts, for example, crossings in places with limited visibility.

Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning.

This study analyzed weekday shopping behavior from a home base to a destination using data from the 4th Keihanshin metropolitan area person trip survey. We first analyzed the relationships between occupation and transportation means, transportation means and travel time, and transportation means and duration of time at the destination. Results of a chi-square test, residual analysis, and correspondence analysis confirmed that employed persons tend to travel by cars while unemployed persons tend to travel by bus or walk. The relationship between travel time and duration of time at the destination was also revealed according to transportation means. Results of a cluster analysis then classified shopping behaviors to expose four patterns. Finally, multiple regression analyzed the degree to which certain variables were related to duration of time at the destination. Results confirmed a strong relationship between duration of time at the destination and travel time. The degree of this factor’s influence on other variables was also clarified. The value of these findings is that the relationship between travel time by means of transportation and the duration of time at the destination was clarified using statistical analysis. We then found a highly accurate equation that estimates the duration of time at a destination from the travel time. If the duration of time at a destination can be estimated, it may be possible to more accurately develop the structure of rest facilities, the number of parking lots, the degree of congestion, and so on, which can be associated with the customer’s usage behavior in a shopping site. This research in this paper contributes to the field of urban analysis and marketing by presenting a new effective method for person trip survey and analysis.

As a well-known urban landscape concept to describe urban space quality, urban street vitality is a subjective human perception of the urban environment but difficult to evaluate directly from the physical space. The study utilized a modern machine learning computer vision algorithm in the urban build environment to simulate the process, which starts with the visual perception of the urban street landscape and ends with the human reaction to street vitality. By analyzing the optimized trained model, we tried to identify urban street vitality’s visual features and evaluate their importance. A region around the Mochou Lake in Nanjing, China, was set as our study area. Seven investigators surveyed the area, recorded their evaluation score on each site’s vitality level with a corresponding picture taken on site. A total of 370 pictures and recorded score pairs from 231 valid survey sites were used to train a convolutional neural network. After optimization, a deep neural network model with 43 layers, including 11 convolutional ones, was created. Heat maps were then used to identify the features which lead to high vitality score outputs. The spatial distributions of different types of feature entities were also analyzed to help identify the spatial effects. The study found that visual features, including human, construction site, shop front, and roadside/walking pavement, are vital ones that correspond to the vitality of the urban street. The consistency of these critical features with traditional urban vitality features indicates the model had learned useful knowledge from the training process. Applying the trained model in urban planning practices can help to improve the city environment for better attraction of residents’ activities and communications.

We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion. Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city. With such pressures on existing public transportation systems, this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services. This research forms a case study of the use of passively collected forms of big data in cities – focusing on Sydney, Australia. Firstly, it examines social media data (Tweets) related to public transport performance. Secondly, it joins this to longitudinal big data – delay information continuously broadcast by the network over a year, thus forming hundreds of millions of data artifacts. Topics, tones, and sentiment are modeled using machine learning and Natural Language Processing (NLP) techniques. These resulting data, and models, are compared to opinions derived from a citizen survey among users. The validity of such data and models versus the intentions of users, in the context of systems that monitor and improve transport performance, are discussed. As such, key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.

In order to improve the calculation quality of 3D coordinate transformation parameters, a robust method for weighted-common-point coordinate transformation method is proposed based on optimization algorithm. The minimum sum of weighted squared coordinate residual, which is the coordinate difference from the transferred coordinates to the known coordinates of common points, is taken as the objective function, and the Nelder-Mead simplex direct search algorithm is utilized to search the optimal weights combination of common points coordinates automatically in calculating coordinate transformation parameters. Taking the alignment and installation of particle accelerator magnets as a typical application scenario, simulated data and measured data are used to verify the proposed method. The results show that the algorithm can effectively reduce the weight of gross errors and poor-quality observations. Compared with the least square method and robust method, the sum of weighted squared coordinate residual of the proposed method is smaller, and the quality of coordinate transformation parameters is better. The proposed method can improve the solution quality of 3D coordinate transformation parameters, and is especially applicable to the situation that the priori precision is unknown and the quality of observation is poor.

The satellite clock bias is one of the important factors that affect the accuracy of navigation and positioning, so establishing a high-precision clock bias prediction model is of great significance to high-precision positioning. Aiming at the problem that satellite clock bias error accumulates by common models over time in short-term prediction, and the easy overfitting and instability of the traditional BP neural network, this paper proposed a model and algorithm of clock bias prediction based on BP neural network optimized by the mind evolutionary algorithm(MEA). First, original clock bias data made once difference to obtain the corresponding once difference sequences. Then, the initial weights and thresholds of the BP neural network were optimized by the mind evolutionary algorithm, the specific steps of using this model for the clock bias prediction were given. The multi-day GPS precision clock bias product data provided by the IGS station is used for experimental analysis. The article used the GPS data for the first 12 h of the day for modeling were listed, and made short-term clock bias prediction within 2, 3, 6 and 12 h. The results showed that the above four periods of prediction precision obtained by using the MEA-BP model were better than 0.36, 0.38, 0.62 and 1.56 ns, respectively. The fluctuation of the prediction error curve was small, and the prediction performance of the new model was better than the three traditional models, which showed the new model is better in practicability and stability in the short-term prediction of clock bias.

Recursive estimation method of cubature kalman filtering(CKF) local polynomial coefficients for phase unwrapping is proposed to retrieve unambiguous unwrapping phase from noisy wrapped phase. First, phase gradient information required is obtained using amended matrix pencil model (AMPM), and then the initial state estimation value of the polynomial coefficients is obtained. Finally, the polynomial coefficients are recursively estimated to obtain the unambiguous unwrapping phase by using the cubature Kalman filter. According to the density of the fringes of the interferograms and the signal-to-noise ratio (SNR) of the interferograms, the row-by-row (or column-by-column) scanning modes or the quality-guide strategy applied in traditional algorithms can be used to guide the cubature Kalman filter to unwrap the wrapped pixels along the suitable paths. The results with the simulated data and the measured data demonstrate that the algorithm in this paper can obtain robust solutions from noisy interferograms, with respect to some other similar algorithms.

In this paper, a 1'×1' bathymetry model BAT_WHU2020 in the range of 75°S—70°N is constructed by using the latest version of the global gravity anomaly model derived from the multi-source satellite altimetry data and the shipboard depths. The accuracy of the model is analyzed and evaluated based on ship depths, existing models and multibeam soundings. The standard deviation of the difference between the proposed model and the ship depths in China Sea and its adjacent areas (104°E—160°E, 0°N—50°N) is about 70 m, which is equivalent to the accuracy of SIO V19.1 model, superior to ETOPO1, DTU10, GEBCO_08 model, and about 30% higher than the accuracy of BAT_VGG model published before, which shows that the method in this paper is reliable, and the data processing is accurate and the accuracy is high. The standard deviation of the differences between BAT_WHU2020 model and ship depths is about 50~65 m globally, and the ratio of the difference within ±200 m is greater than 95%. It is showed that the accuracy of BAT_WHU2020 model is equivalent to SIO V19.1, better than ETOPO1, DTU10, GEBCO_08 model, and improved about 27%~36% from the BAT_VGG model. Comparing to SIO V19.1 model, the standard deviation of the model differences is about 90~110 m, about 90% of the grid differences is within 200 m, and about 95% is within 300 m . Finally, the effects of crustal isostasy and high order terms in the Parker's formula on the accuracy of the results, the accuracy of the model compared to multibeam soundings, and the spatial resolution are discussed. It indicates that the spatial resolution of BAT_WHU2020 model is about 10~18 km, and the relative accuracy is about 5%~6% around the Mariana trench and Macquarie ridge.

Spectral library-based hyperspectral sparse unmixing technology has received attention in recent years, which uses spectral samples in the spectral library as endmembers and transforms the unmixing problem into a sparse representation problem. However, due to differences in the measurement environment, the actual endmembers of the hyperspectral image to be unmixed tend to differ from the corresponding spectral signatures in the spectral library. In this paper, an unmixing algorithm named spectral difference sparse constrained collaborative sparse regression is proposed. Firstly, we assume that the spectral differences have sparse property, and a spectral library correction model is established, which can make the spectral library be adaptively adjusted during the unmixing process; Then, the spectral library correction model is combined with the collaborative sparse regression unmixing model to establish a sparse unmixing model considering spectral differences; Finally, an iterative optimization solution based on the alternating direction method of multipliers is given. Synthetic and real hyperspectral data are used to verify the performance of different algorithms. The results show that the proposed algorithm is more effective than the compared algorithms in the presence of spectral library mismatches.

Aiming at how to select the optimal carrier-phase linear combinations for triple-frequency BDS, a fuzzy clustering analysis method based on the theory of triple-frequency GNSS carrier phase combination is proposed. Firstly, the combination which satisfies the conditions of extra-wide-lane and narrow-lane combination and has different lane-number, ion-number and noise amplification factor is constructed, and the theoretical optimal combination with optimal parameters is assumed. Then, the extra-wide-lane combinations and narrow-lane combinations satisfying the conditions has been clustered and sorted by fuzzy clustering analysis method. Finally, according to the clustered sequence of each combination with the assumed theoretical optimal combination, the order of selection for triple-frequency BDS optimal combinations is determined. The results show that the fuzzy clustering analysis method achieves the selection of the optimal combined observations by clustering the combined observations.The results of geometry-free TCAR algorithm also verify the correctness of fuzzy clustering analysis results.

Power towers are important parts of power infrastructure, and it is indispensable to detect them. In view of the low precision and poor result of detection algorithms for power towers in remote sensing imagery, this study improves Faster R-CNN based on deformable network and transfer learning. And then we propose a new detection framework for power tower in remote sensing imagery. The framework includes a feature extraction sub-network and an object detection sub-network. The feature extraction sub-network uses deformable network model, which reconstructs the convolutional layer, to improve the model's feature extraction ability of the power towers with geometric deformation. The model parameters obtained from the feature extraction sub-network training are transferred to object detection sub-network, which accurately obtains position of power towers through RPN network,deformable area pooling and nms algorithms. Finally, the object detection sub-network is finely tuned and achieve high-precision detection for power towers in remote sensing image. The results show that in the test datasets AP0.5, AP0.6 and ACC are 0.886 1, 0.839 6, 0.894 8 which are at least higher 0.2 than SSD YOLOv3, Faster R-CNN. It can be seen from the comparative experiment that this method for power towers detection has great application potential.

To develop the accuracy for predicting SCB based on the the problem that the wavelet neural network (WNN) model fails to select the appropriate wavelet function according to the actual situation, an wavelet neural network for Optimal Wavelet Function based on Shannon entropy-energy ratio to predict SCB is proposed herein. The wavelet coefficients are obtained by carring on the continuous wavelet decomposition to the clock a once difference sequences. Then, the energy value and Shannon's entropy value of the wavelet coefficient are calculated respectively, and the “Shannon's entropy-energy ratio” (SEE) is taken as the evaluation index for the selection of the optimal wavelet function to induct select the most suitable wavelet function as the activation function of WNN model. Finally, the optimal WNN model is used to predict SCB, and the predicted results are compared and analyzed. The results show that the evaluation index can accurately guide WNN model to choose the appropriate wavelet function according to the actual situation of SCB, improve the prediction accuracy and applicability of WNN model, and enable the model to realize high accuracy SCB prediction.

Traditional convolutional neural network almost cannot obtain satisfactory classification results of the remote sensing images due to the large differences in the size and spectral characteristics of the objects. In addition, the complex background environment will also bring interference to the classification. Aiming at this problem, the multilevel attention fusion U-Net (MAFU-Net) is presented. To enhance the correlations between different pixels and channels, the attention module is applied to extract and process semantic information at different levels, which further improves the classification performance of the network under complex background. In order to verify the effect of the proposed network in the classification of remote sensing images, the experiments were carried out on Vaihingen dataset of ISPRS, Beijing and Henan dataset of GF 2, respectively, and several different semantic segmentation networks are used for comparison. The experimental results show that the proposed network has fewer parameters and lower computational complexity, but can achieve higher classification accuracy in the least time, which means the network is highly practical.In addition, the feature visualization was fully utilized to analyze the classification performance of MAFU-Net and other networks, and the results also show that most deep learning network models are difficult to be deduced according to the accurate mathematical principles. It is also difficult to explain why a particular network fails in a particular dataset. Therefore, the further study or more advanced visualization and quantification criteria are required to analyze and evaluate specific deep learning models and network performance, then the more advanced model structure can be designed.

Accessibility is a representative indicator for evaluating the supply of bus system. Traditional studies have evaluated the accessibility from different aspects. Considering the interaction among land use, bus timetable arrangement and individual factors, a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data. The rationale of the proposed model is verified by a case study of bus system in Shenzhen, China, which is carried out to find the spatial and temporal discrepancy of service of bus system. It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development, average bus speed and bus facilities all have positive effects on accessibility of bus system. These findings provide significant reference for transport planning and policy-making. The proposed model is not limited to accessibility measuring of bus system, but also applicable to other travel modes.

A point zero represents a central geographical location of a city, which is essential for not only measuring distances, but also understanding the historical and/or cultural origin of a city. Although extensive studies have focused on delineating city centers, to our knowledge, no studies have paid attention to determining the location for a point zero. Here, our goal is to investigate various potential approaches for identifying such a location. Specifically, three typical approaches, geometric-based, topological-based, and thematic-based, are proposed to recommend point zeros and different scales, administrative divisions, kernel density surfaces, and regions enclosed by ring roads are used for analysis. The effectiveness of different approaches and scales are evaluated and compared by calculating an offset distance between recommended and actual point zero locations in 20 Chinese cities. Using the different approaches, the average offset distance for most Chinese cities is 2–4 km, and the thematic-based approach performs best. Both the topological-based and thematic-based approaches are less sensitive to the analyzed scale than the geometric-based approach. The results indicate that it is possible to determine the approximate location for a point zero from geographical data. Based on the buffer zone of such a location, an actual point zero may further be recommended.

Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems. These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy, but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover. Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View (GSV) images, made accessible by the Google Street View Image API. Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density, and it facilitates an analysis performed at the street-level. In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index. We deployed this image processing method and, using GSV images as a high-resolution GIS data source, we computed and mapped the green index of Milwaukee County, a 3,082 $$k{m^2}$$ urban/suburban county in Wisconsin. This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management, as well as for researchers investigating the correlation between environmental factors and human health outcomes.

Streets and physical layouts can be inherent in a sustainable city by emphasizing the use of space with planned strategies by promoting the movement of people and social behaviors for enhancing an economic structure. An analysis of the space layout is beneficial for defining the urban areas that can affect street behavior. A GIS incorporated with a space syntax can help to propagate the effectiveness of a spatial analysis in a study on urban spaces. The integration of the computational pieces of both a GIS analytical tool and space syntax parameters will stimulate research oriented urban systems and spatial morphologies combined with a spatial database. However, the development of livability can be affected by a set of parameters that define the used space. Thus, this paper was aimed at examining the space syntax parameters for space visualization to evaluate street behavior using the GIS processing and space syntax methods. In this study, a spatial analysis was used to generate spatial information on traffic volume, while a space syntax was used to test the street behavior. Also, a predictive analysis was conducted to identify the correlation between traffic behavior and a set of parameters. The results showed that integration and direct visibility were significant to the traffic volume. Most of the streets that were linked to the commercial area showed high integration and direct visibility, which increased by more than half, compared with the unlinked areas. Based on the statistical analysis, both parameters recorded aprobability of less than 5%. The results showed that any space designed with a continuous, direct and clear traffic volume will lead to increased integration and direct visibility, thus influencing a natural vehicle movement.

It is proposed that a multi-scale representation model of geographic polyline based on Head-Tail break of amount of information. Firstly, a geographic polyline is transformed into a Fourier shape descriptors, and the amount of information in frequency domain is defined using Shannon’s information entropy theory. Secondly, Head-Tail breaks is introduced to determine the demarcation points of information content of the polyline, and a function is designed to estimate the Fourier truncating frequency corresponding to each demarcation point. Finally, the traditional radical model based on the frequency information amount of polyline is introduced to measure the key scales and to achieve hierarchical multi-scale representation of polylines. The experiments of contour and coastline demonstrate that the model proposed can effectively simplify geographic polylines according to the set scale, and the simplification results of different target scales exist good degree of differentiation and hierarchy. At the same time, under the same overlap ratio between the simplified result and the original geographic polyline, the model proposed is better than the traditional simplification algorithm.

At present, the monitoring research of synthetic aperture radar differential interferometry (D-InSAR) in mining areas is mostly limited to taking surface movement as the object. Field measurement is usually needed to obtain other displacement deformation factors of the surface. In view of this deficiency, SMD model was constructed to map the regional surface movement and deformation(SMD). Here are the value scan besolved by SMD:①the inclination, curvature in arbitrary direction, the maximum value of inclination, curvature and its directions; ②horizontal movement and deformation of the surface in any direction in horizontal or near-horizontal coal mining, the maximum value of horizontal movement, horizontal deformation and its directions in horizontal or near-horizontal coal mining. The SMD model maps the inclination and curvature of the ground surface according to the directional derivative principle on the basis of the vertical deformation map. The horizontal movement and deformation of the surface are calculated according to the prior law of mining subsidence. The simulation experiment shows that the model can obtain the surface movement and deformation with high accuracy.SMD model combined with time-series InSAR technology was used to analyze the spatial-temporal evolution process of surface deformation and the subsidence and deformation reduction effect of grouting mining in Huaibei. The analysis showed that the grouting activity could effectively control the surface deformation. The relationship between the average grouting volume, the advancing distance of the working face and the maximum subsidence rate can be described by the multivariate linear regression model.SMD model can be used to analyze the comprehensive situation of regional surface deformation and provide a strong basis for the evaluation of the impact of mining subsidence regionally on construction.

Radiometric normalization, as an essential step for multi-source and multi-temporal data processing, has received critical attention. Relative Radiometric Normalization (RRN) method has been primarily used for eliminating the radiometric inconsistency. The radiometric transforming relation between the subject image and the reference image is an essential aspect of RRN. Aimed at accurate radiometric transforming relation modeling, the learning-based non-linear regression method, Support Vector machine Regression (SVR) is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN. To evaluate the effectiveness of the proposed method, a series of experiments are performed, including two synthetic data experiments and one real data experiment. And the proposed method is compared with other methods that use linear regression, Artificial Neural Network (ANN) or Random Forest (RF) for radiometric transforming relation modeling. The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.

The layout of seafloor control points is an important part of constructing the marine space-time frame. And the reliable marine datum positioning models and methods are the premise and basis for the layout of high-precision seafloor control points. The widely used vessel sailing surveying has both flexibility and controllability. However, there are also the inevitable abnormal disturbance effects of the vessel, which leads to the distortion of the joint positioning model of seafloor control points. To solve this problem, a GNSS/acoustic joint positioning algorithm based on adaptive weight filtering is proposed. First, the GNSS/acoustic joint positioning mathematical model for combined sea surface and underwater observation processes is derived. Then the criterion of judging the dynamic disturbance when using the adaptive filter to solve the joint positioning model is studied, and the method of constructing adaptive factors for each state parameter is given. Finally, the simulated and the measured data were used to carry out the experiment. Results show that the adaptive filtering algorithm can effectively improve the abnormal influence of state disturbance on GNSS/acoustic joint positioning, and promote its positioning stability and accuracy. When the corresponding adaptive factors of various state parameters are constructed reasonably, the best performance of the filtering effect is proved.

The underwater submersible track in the adaptation area where the gravity characteristics change obviously can ensure the effective implementation of gravity-assisted navigation. Therefore, in the gravity matching navigation stage, the path planning of the submersible is very important. In this paper, the adaptive submarine navigation area is adaptively divided according to the gravity statistical characteristic parameters, and the adaptive and non-adaptive area labels are given. Then the artificial potential field algorithm is introduced based on the ant colony algorithm for the path planning. Reconstructing the heuristic function avoids the local optimal problem of the ant colony algorithm. At the same time, the maximum-minimum ant colony system is used to improve the pheromone update rule, which prevents the “premature” phenomenon. The simulation results show that the proposed ant colony-potential field algorithm can effectively solve the path optimization problem of underwater submersible in gravity-assisted navigation and improve the feasibility of the solution.

For the problem of fitting the independent variables with error, orthogonal distance least squares and weighted total least squares two independent approaches can be employed to solve it. However, the weighted total least square is unlike orthogonal distance least squares. It does not taking into account the orthogonal geometry information, namely, the line segment consisted of observation point and fitted point is vertical to fitted object. Aimed to solve this problem, a nonlinear equality constrained total least squares adjustment model is proposed, in which total least squares is combined with orthogonal geometry information that has been transformed into a nonlinear equality constraints function with unknown corrected errors. After the function model and the nonlinear equality constraints function are linearized, the Lagrange multiplier method is introduced to derive the calculation formula for estimating parameter and assessing accuracy. Two iterative algorithms are given correspondingly. The suggested method and the designed algorithm are tested by the example of fitting a straight-line. The results show that, ①the proposed model and the iterative algorithm are feasible; ②compared with weighted least squares and weighted total least squares, the sum square of orthogonal distance from the measured point to the fitted line calculated by the new method is the smallest value; ③this orthogonal distance is equal to the distance from the measured point to the corrected point calculated by the suggested method, and the other two methods are not like this.

Coordinated universal time offset error(UTCOE) which is the error of broadcasted coordinated universal time offset(UTCO) has become one of the focuses that global navigation satellite system(GNSS) provider and its users are concerned in recent years. This paper studies UTCOE definition, proposes an indirect assessment method of UTCOE based on Coordinated Universal Time(UTC) kept by National Time Service Center( UTC(NTSC)) and circular T bulletin published by International Bureau of Measurement(BIPM).The validity of the method is verified by using direct assessment method of GPS UTCOE. Furthermore, uncertainty budget of the assessment method is also given. The UTCOE of BDS, GPS and GLONASS are assessed utilizing the monitoring data from the running GNSS system time offset monitoring system at NTSC. During the assessment cycle throughout 2018, the root mean square (RMS) of BDS UTCOE is 17.33 ns. The 95% statistical value of UTCOE of BDS, GPS and GLONASS are 35.47、2.08 and 8.64 ns, respectively. The results show that the UTCOE of BDS, GPS and GLONASS all meet their own standard performance specifications. The UTCOE assessment results of GPS are far superior to its performance commitment. GLONASS is the second. BDS UTCOE assessment results are critical and show obvious systematic error characteristic. So, the UTCOE of BDS has a large room for promotion.

Global positioning system interferometric reflectometry (GPS-IR) is a new remote sensing technique that can be used to estimate near-surface soil moisture from signal-to-noise ratio (SNR) data recorded by a measurement receiver. Considering that there are few studies on the inversion of soil moisture by multi-satellite combination, a multi-star linear regression soil moisture inversion model is proposed. The experiment shows that: ①The multi-satellite combination inversion mode can more comprehensively reflect the soil moisture information within the effective monitoring range near the station, and effectively improve the phenomenon that the inversion process is prone to abnormal jump when using single satellite inversion. At the same time, it improves the accuracy of soil moisture inversion during sudden rainfall periods. ②When the number of combined satellites reaches 6 or more, the correlation coefficient between the inversion result and the soil moisture reference value is greater than 0.9, which is at least 20.8% higher than that of a single satellite.

The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task.

The construction of the voxel model is an important part of computer ionospheric tomography (CIT) method. It will no doubt affect the accuracy of the CIT method. However, most existing researches focus on how to improve the image’s accuracy via inversion algorithm and few of them are concerned with the grid construction solution. This paper intends to improve accuracy by making some optimization on the voxel model. Some preliminary experiments were first carried out to explore the relationship between the accuracy of the image and its corresponding resolution. Then, based on the findings, a new CIT method, which is to improve the accuracy of the tomographic image of the interesting area at the price of reducing the resolution of the non-interesting area, was proposed. After that, some further experiments were taken to validate the new method. After comparing various accuracy indicators, such as root mean square error (RMSE), absolute mean error (MAE), 68% percentile, 95% percentile and stand deviation, the new method is showed to be better than the one that is not using the method proposed in this paper. According to our experiments, the new method is able to decrease RMSE/MAE of the tomographic image by 15% to 40%.

The integer characteristics of the ambiguity can be recovered after the correction of fractional cycle biases (FCB), which can significantly shorten the initialization time of precise point positioning (PPP).However, the ambiguity resolution model of the clientend needs to be consistent with serverend, which may introduce the problem that the client maybe unable to use different FCB products and increases the transmission pressure of the server link. A unified PPP ambiguity resolution method is proposed that based on three PPP models,i.e.,ionosphere-free combination model, uncombined model without prior ionosphere constraint and uncombined model with prior ionosphere constraint.Different clients can use the same FCB product to achieve fast ambiguity resolution.A globally distributed network consisting of 116 MGEX stations is used to generates three kinds of FCB products at the server end, and 50 additional stations are selected at the client end for verification.The experimental results show that the new method solves the problem of ambiguity resolution of PPP at client end when facing different FCB products. Comparable performance with the traditional method can be achieved with regards to in positioning accuracy, convergence time and success rate.

Aiming at the complex environment such as missing and noise in power line cloud data, a precise power line extraction method based on model residual clustering from LiDAR point is proposed. Firstly, the near-ground points are removed according to the normalized elevation threshold segmentation. The power line points are roughly extracted using adaptive dimension features and directional features. Secondly, the improved modeling method is adopted to determine the model residual error with the constraint condition of the parabolic model. The result obtained by density clustering on the model residual error is used to extract the single power line point. Finally, the influence of the selection of key parameters on the extraction results is discussed. Two experimental results show that the method can quickly extract power line from point cloud with partial missing and noise interference, without prior knowledge such as the number of power lines and density of point cloud, etc. Which has good applicability for different types of bundle conductor extraction. the accuracy of single power line extraction is more than 99.17%, the maximum error of model fitting is 0.167 m, and the maximum mean square error of model fitting is 0.079 m.

Urbanization has triggered great development and changes in underground space. Exploring the types and positions of underground targets is of vital importance to urban underground security and utilization. GPRs (ground penetrating radar) are widely used in exploring underground space because of its advantages of rapid data collection, convenience, high imaging resolution and non-destructive inspection. However, the heavy manual interpretation costs of object detection from GPR limit the GPR applications in large-scale urban underground objects detection. This paper analyzes and determines seven typical types of urban road underground target that can be detected in GPR images (e.g. rainwater wells, cables, etc.). According to the characteristics of its reflected signals, the underground target in the GPR data of the 400 MHz band acquired by GSSI SIR30 in a typical urban road environment are labeled to construct the training dataset with seven categories and 3033 training samples. With the transfer learning method, the pre-trained Darknet 53 network parameters are fine-tuned, and the end-to-end YOLOV3 detection method is used to automatically extract and locate the underground targets. Finally, the experimental verification was carried out using the GPR data of the 400 MHz band collected by GSSI SIR30 in Caitian Road, Futian District, Shenzhen. Experiments show that the proposed deep learning detection method detects the buried objects from GRP data effectively, in terms of 85% of recall and precision,and the detection speed of 16FPS.

Road information extraction based on vehicle trajectory data is one of the hotspots and difficulties in the field of geographic information. The rapid development of depth learning provides a new idea and method for solving this problem. Aiming at the problem of roadway-level road extraction based on vehicle trajectory data, this paper introduces the generative adversarial nets in the field of deep learning, uses residual network to construct deep network and multi-scale receptive field to perceive different details of trajectory data, and constructs roadway-level road extraction model under the constraint of trajectory direction based on conditional generative adversarial nets. Firstly, the orientation-color mapping rasterization conversion method is proposed to transform the trajectory orientation information into HSV color space. Then, the parameters of the model are learned with the sample data. Finally, the trained model is applied to three experimental areas of Zhengzhou, Chengdu and Nanjing to extract the road data at the roadway level. The experimental results showed that the proposed method can effectively extract the complete road data at the roadway level.