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The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people's wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. [ABSTRACT FROM AUTHOR]

Abstract Since Google Earth was first released in 2005, it has attracted hundreds of millions of users worldwide and made a profound impact on both academia and industry. It can be said that Google Earth epitomized the first-generation of Digital Earth prototypes. The functionalities and merits that have sustained Google Earth's lasting influence are worth a retrospective review. In this paper, we take the liberty to conduct a bibliometric study of the applications of Google Earth during 2006–2016. We aim first to quantify the multifaceted impacts, and then to develop a structured understanding of the influence and contribution associated with Google Earth. To accomplish these objectives, we analyzed a total of 2115 Scopus publication records using scientometric methods and then proceed to discussion with a selected set of applications. The findings and conclusions can be summarized as follows: (1) the impact of Google Earth has been profound and persistent over the past decade. Google Earth was mentioned in an average of 229 publications per year since 2009. (2) Broadly, the impact of Google Earth has touched upon most scientific disciplines. Specifically, during 2006–2016, Google Earth has been mentioned in 2115 publications covering all of Scopus's 26 subject areas; (3) the influence of Google Earth has largely concentrated in GIScience, remote sensing and geosciences. The extended influence of Google Earth has reached a wider range of audiences with a concentration in fields such as human geography, geoscience education and archaeology. [ABSTRACT FROM AUTHOR]

Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas. [ABSTRACT FROM AUTHOR]

Chongming Island in China is currently undergoing a rapid urbanization and an increasing environmental pressure due to its fast-paced social economic development. Owing to natural silts deposit phenomenon as well as artificial land reclamation practices, the Island is also experiencing an expansion phenomenon. The synergy between these natural and artificial phenomena results in a rapidly changing landscape on Chongming Island. Consequently, the tools and methods for a rapid and cost-effective detection and assessment of related issues are urgently needed to ensure a harmonious and sustainable development of the Island. We herein investigate the urban growth and the landscape pattern change in relation to the Island’s expansion phenomenon and the associated complexity of land use/cover change. Our investigation is based on a time series of Landsat satellite images spanning the past 34 years. The methodological approach adopted in the present study combines vegetation indices, images textural features and social statistics data in an object-oriented classification framework. With Chongming Island expanding by an annual rate of 0.9% between 1979 and 2013, we found that the proportion of vegetation area to the total area decreased from 71 to 45%, whereas the proportion of built-up area to the total area increased from 5 to 19.9%. The urban area expanded about six times from 1979 to 2013, and during the same period, the Island’s population did not change significantly. The urban spatial expansion of Chongming Island caused distinct expansion intensity index for each intervals, and significant fragmentation and diversity in the landscape pattern between 1979 and 2013. It was also found that the rapid urbanization process took place at the expense of landscape pattern changes at any time within the study period. This is a strong indication that besides the natural geographic element, economic development and policy orientation were the dominant driving factors. If the current rate of urban expansion is to be maintained and the vegetation cover is to keep decreasing at an annual rate of 0.1% (period 1979–2013), their combined effects would profoundly alter the ecological environment in the long term. These findings provide a basic objective and scientific information for knowledgeable decision-making and policy formulation regarding regional planning and management to ensure harmonious transition of Chongming towards ecologically oriented development. [ABSTRACT FROM AUTHOR]

Accurate estimation of phytoplankton chlorophyll-a(chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-aconcentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-aconcentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [Rrs−1(661.6) –Rrs−1(706.7)] [Rrs−1(714.8) –Rrs−1(682.2)]−1was calibrated after band tuning, whereRrs−1represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-aconcentration with a root mean square error (RMSE) of 1.47 μg l−1. To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-avariation with an RMSE of 1.35 μg l−1. Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-aconcentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery. [ABSTRACT FROM AUTHOR]

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. [ABSTRACT FROM AUTHOR]

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods. [ABSTRACT FROM AUTHOR]

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. [ABSTRACT FROM AUTHOR]