Search

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F 1 -scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model's performance, emphasizing the effectiveness of these components in capturing detailed contextual cues. [ABSTRACT FROM AUTHOR]

In recent decades, the application of GIS and RS in archaeological and cultural heritage (ACH) has witnessed a notable surge both in terms of quantity and scope. During the initial implementation period (2016–2021) of the Digital Belt and Road Heritage (DBAR-Heritage) working group, several instances of GIS-RS-based applications in support of cultural heritage conservation have merged. In this paper, in order to discuss the great potential of GIS and RS on the Silk Road, an overview of GIS- and RS-based applications in ACH is first presented. In a substantial portion of the published scientific literature, the identification and comprehension of archaeological sites, the monitoring and risk assessment of cultural heritage, and the management and visualization of cultural heritage data are highlighted. Following this, five illustrative case studies from the DBAR-Heritage working group are presented to exemplify how the integration of GIS and RS serves as key approaches in recognizing and appreciating cultural heritage. These selected case studies showcase the utilization of multi-source data for the identification of linear sites; detailed, refined monitoring and assessment of the Angkor Wat heritage; and the reconstruction of the Silk Road routes. These instances serve as the cornerstone for highlighting current trends in GIS and RS applications in ACH along the Silk Road. These methodologies efficiently integrate multi-source geospatial data and employ multidisciplinary approaches, ultimately furnishing sophisticated and intelligent tools for the exploration and management of archaeological and cultural heritage in the era of Big Earth Data. Subsequently, a comprehensive discussion on the merits and challenges of GIS and RS applications in ACH is presented, followed by an exploration of the current application trends. Finally, the prospects for the widespread application of GIS and RS in ACH along the Silk Road are outlined in accordance with the operational plan of DBAR-Heritage during its second implementation phase. [ABSTRACT FROM AUTHOR]

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules. [ABSTRACT FROM AUTHOR]

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic representations at different levels are difficult to capture accurately at the same time, and then the extraction results decline to become suboptimal, especially on narrow areas and boundaries. To address the above-mentioned problem, a multiscale successive attention fusion network, named MSAFNet, is proposed to efficiently aggregate the multiscale features from two aspects. A successive attention fusion module (SAFM) is first devised to extract multiscale and fine-grained features of water bodies, while a joint attention module (JAM) is proposed to further mine salient semantic information by jointly modeling contextual dependencies. Furthermore, the multi-level features extracted by the above-mentioned modules are aggregated by a feature fusion module (FFM) so that the edges of water bodies are well mapped, directly improving the segmentation of various water bodies. Extensive experiments were conducted on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. Numerically, MSAFNet reached the highest accuracy on both QTPL and LoveDA datasets, including Kappa, MIoU, FWIoU, F1, and OA, outperforming several mainstream methods. Regarding the QTPL dataset, MSAFNet peaked at 99.14% and 98.97% in terms of F1 and OA. Although the LoveDA dataset is more challenging, MSAFNet retained the best performance, with F1 and OA being 97.69% and 95.87%. Additionally, visual inspections exhibited consistency with numerical evaluations. [ABSTRACT FROM AUTHOR]

Karez systems are ancient hydraulic works that use underground waterways to divert water by gravity and have historically been popular in arid regions across Central Asia. Karez systems have undergone thousands of years of development and have been used for irrigation in 40 countries and regions worldwide. Although there are different opinions about the origin of karezes, the role and significance of karezes are similar. The Turpan Basin is a relatively closed inland basin in China, far from the ocean, with a very dry climate and high evaporation rates. However, due to the ice and snow meltwater of the Tianshan Mountains, the groundwater resources in the basin are abundant. Karezes are an important support for Turpan's farming civilization and tourism culture and represent a great masterpiece of how people in arid areas have used the natural environment. This study used historical CORONA images to visually interpret the karez system in the 1970s and compared it with the karez system in 2020 to analyze the spatial distribution variation characteristics of the karezes. The impact of land use/land cover change on the karezes was also analyzed. The results showed that from 1970 to 2020, as the population grew, there was an increase in arable land and built-up areas while the water area decreased. In general, the increase in arable land and built-up areas, the decrease in water area, and the increase in the number of electromechanical wells have combined to reduce the number of karez systems. Based on the CORONA image from 1970, it is possible to visualize the shaft area that existed in 1970 but did not exist in 2020. Some karez shafts that existed in bare terrain areas in 1970 were truncated when the land use/land cover type changed to arable land. The area where the disappeared karez shafts were located is approximately 87.77 square kilometers. Through the study of the changes in the spatial distribution of karezes and the impact of land use/land cover change on karezes, this research provides a valuable reference for the construction of karez conservation areas or urban planning. The investigation of the distribution of historical karezes is of great significance for studying the changes in karezes and excavating the historical and cultural value of karezes. [ABSTRACT FROM AUTHOR]

Vegetation phenology is a sensitive indicator of climate change, and can help understand the response of vegetation cycles to climate, which is important for understanding the impact of global climate change on terrestrial ecosystems. In this study, based on the enhanced vegetation index (EVI) time-series data, derived from the moderate-resolution imaging spectroradiometer (MODIS) data, the climate parameters were extracted using the Savitzky–Golay (S–G) filtering method to explore the spatial and temporal variation characteristics of the vegetation phenology in an agro-pasture ecotone in China, from 2000 to 2020. In addition, the response characteristics of the vegetation phenology to the climate elements (temperature and precipitation) were also analyzed. The results showed the following: (1) The start of the growing season (SOS) was widely advanced, and that was caused by climate change. The end of the growing season (EOS) was delayed, and the length of the growing season (LOS) was gradually extended with a large interannual fluctuation in the SOS and the LOS in the region; (2) the SOS showed significant negative correlations with the air temperature and precipitation. Precipitation was mainly positively correlated with the EOS, but there was no significant difference in the correlation between temperature and the EOS. In general, pre-season precipitation is the main driver of the vegetation phenology, while the influence of temperature on the phenology is less obvious; (3) in the semi-arid area and arid area, the phenology was mainly influenced by precipitation. The response of the vegetation phenology to the temperatures in different temperature zones was found to be regular, showing high spatial differences. In general, the higher the cumulus temperature, the lower the negative effect of the temperature on both the SOS and EOS. These results may provide new reference to study the non-systematic changes of the vegetation phenology in response to climate change. [ABSTRACT FROM AUTHOR]

Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The emergence of the transformer intrigues the revolution of vision tasks with its impressive scalability in establishing long-range dependencies. However, the local patterns, such as inherent structures and spatial details, are broken with the tokenization of the transformer. Therefore, the ICTNet is devised to confront the deficiencies mentioned above. Principally, ICTNet inherits the encoder–decoder architecture. First of all, Swin Transformer blocks (STBs) and convolution blocks (CBs) are deployed and interlaced, accompanied by encoded feature aggregation modules (EFAs) in the encoder stage. This design allows the network to learn the local patterns and distant dependencies and their interactions simultaneously. Moreover, multiple DUpsamplings (DUPs) followed by decoded feature aggregation modules (DFAs) form the decoder of ICTNet. Specifically, the transformation and upsampling loss are shrunken while recovering features. Together with the devised encoder and decoder, the well-rounded context is captured and contributes to the inference most. Extensive experiments are conducted on the ISPRS Vaihingen, Potsdam and DeepGlobe benchmarks. Quantitative and qualitative evaluations exhibit the competitive performance of ICTNet compared to mainstream and state-of-the-art methods. Additionally, the ablation study of DFA and DUP is implemented to validate the effects. [ABSTRACT FROM AUTHOR]

As a large-scale irrigation and water conservancy project in ancient times, karez are common in Central Asia and arid regions with a history of thousands of years. Turpan, which is located in the Xinjiang Uyghur Autonomous Region, has the most extensive and concentrated distribution of karez shafts in China. There are tens of thousands of shafts, some of which are in use and are living cultural heritage. According to radiocarbon (14C) dating, some karezs are over 600 years old. The karez is of great significance to the research on geology, hydrology, oasis, climate change, and development history of karez in Turpan. With the development of the population, arable land, industrialization, and urbanization, karez systems are facing the risk of abandonment. Detailed karez distribution mapping or dynamic monitoring data are important for their management or analysis; although there are related methods, due to Turpan's large desert and "Gobi" environments, field surveys are time- and energy-consuming, and some areas are difficult to access. Precise shaft locations and distribution maps are scarce and often lack georeferencing. The distribution and preservation of karez have not yet been fully grasped. In this study, we evaluated the effectiveness of You Only Look Once version 5 (YOLOv5) in automatically detecting karez in high-resolution images of the Turpan region. We propose post-processing steps to reduce the false karez identified by YOLOv5. Our results demonstrate the feasibility of using YOLOv5 and post-processing techniques to detect karez automatically, and the detected results are sufficient to capture the linear alignment of karez. Target detection based on YOLOv5 and post-processing can greatly improve automatic shaft identification and is therefore useful for the fine mapping of karez. We also applied this method in Shanshan County (for which no detailed mapping data on karez has been obtained before) and successfully detected some karez that had not been archived before. The number of shafts in Turpan is 82,493. Through DBSCAN clustering, it was identified which karez line belonged to which shaft; the number of sections of karez that have been used is 5057, which have a total length of 2387.2 km. The karez line obtained was overlaid with the crop-land data, and the positional relationship between the karez line and the crop land was analyzed. The cultivated area is basically surrounded by karez. Our method can potentially be applied to construct an inventory for all karez shafts globally. [ABSTRACT FROM AUTHOR]

Northern China's agropastoral ecotone has been a key area of desertification control for decades, and digital maps of its soil organic carbon (SOC) stocks are needed to reveal the gaps between the actual SOC levels and baseline to support land degradation neutrality (LDN) under the Sustainable Development Goals. However, reliable soil information is scarce, and accurate prediction is hindered by the fragmented landscape, which is a dominant characteristic of desertified land. To improve the patchiness identification and accuracy of SOC prediction, we conducted field surveys and collected low-altitude aerial images along the desertification degrees (severe and extremely severe, moderate, slight) in the Horqin Sandy Land. Linear regressions were performed on the relationships between the normalized difference vegetation index and the fractional vegetation cover (FVC) extracted from aerial images, and regression kriging was applied to predict SOC stocks based on the soil-forming factors (vegetation, climate, and topography). Our prediction and cross-validation showed that the fragmented structure and prediction accuracy of SOC stocks were both greatly improved for desertified land. The FVC (R2c = 0.94) and evapotranspiration (R2c = 0.86) had significant positive effects on SOC stocks, respectively, with indirect and direct causal relationships. Our results could provide soil information with better patchiness and accuracy to help policymakers determine the future LDN status in this fragmented desertification landscape. As drone technology becomes more available, it will fully support digital mapping of soil properties. [ABSTRACT FROM AUTHOR]

The increasing availability of multiplatform, multiband, very-high-resolution (VHR) satellite synthetic aperture radar (SAR) data has attracted the attention of a growing number of scientists and archeologists. In particular, over the last two decades, archeological research has benefited from SAR development mainly due to its unique ability to acquire scenes both at night and during the day under all weather conditions, its penetration capability, and the provided polarimetric and interferometric information. This paper explored the potential of a novel method (nonlocal (NL)-SAR) using TerraSAR-X (TSX) and Constellation of Small Satellites for Mediterranean Basin Observation (COSMO)-SkyMed (CSK) data to detect buried archeological remains in steep, rugged terrain. In this investigation, two test sites were selected in southern Tunisia, home to some of the most valuable and well-preserved limes from the Roman Empire. To enhance the subtle signals linked to archeological features, the speckle noise introduced into SAR data by the environment and SAR system must be mitigated. Accordingly, the NL-SAR method was applied to SAR data pertaining to these two significant test sites. Overall, the investigation (i) revealed a fortified settlement from the Roman Empire and (ii) identified an unknown urban area abandoned during this period via a field survey, thus successfully confirming the capability of SAR data to reveal unknown, concealed archeological sites, even in areas with a complex topography. [ABSTRACT FROM AUTHOR]

The observation of solid earth tides (SET) provides an important basis for understanding the structure of the earth's interior, and has long been the focus of research in geoscience. However, actually, there still exist some limitations in capturing its global-scale information only with ground stations. Remote sensing technology can realize large-scale deformation monitoring of high point density constantly. However, it is still difficult for the artificial satellite system to meet the requirements of SET monitoring in terms of field of view and temporal resolution now. In this work, the moon is hypothesized as a new platform for SET observation combined with interferometric synthetic aperture radar (InSAR) technology. Based on the tidal model and lunar ephemeris, the spatial and temporal characteristics of the SET from the lunar view were analyzed. Furthermore, the calculations demonstrate that more abundant SET information can be observed in this view. After comparing various observation modes, the single-station with repeat-pass differential InSAR was selected for this simulation. We mainly considered the restriction of observation geometry on moon-based InSAR under three signal bandwidths, thereby providing a reference for the sensor design. The results demonstrate that the moon-based platform offers the potential to become an optimal SET observation method. [ABSTRACT FROM AUTHOR]

The alpine treeline, as an ecological transition zone between montane coniferous forests and alpine meadows (two ecosystem states), is a research hotspot of global ecology and climate change. Quantitative identification of its elevation range can efficiently capture the results of the interaction between climate change and vegetation. Digital extraction and extensive analysis in such a critical elevation range crucially depend on the ability of monitoring ecosystem variables and the suitability of the experimental model, which are often restricted by the weak intersection of disciplines and the spatial-temporal continuity of the data. In this study, the existence of two states was confirmed by frequency analysis and the Akaike information criterion (AIC) as well as the Bayesian information criterion (BIC) indices. The elevation range of a transition for the two ecosystem states on the northern slope of the Bogda was identified by the potential analysis. The results showed that the elevation range of co-occurrence for the two ecosystem states was 2690–2744 m. At the elevation of 2714 m, the high land surface temperature (LST) state started to exhibit more attraction than the low LST state. This elevation value was considered as a demarcation where abrupt shifts between the two states occurred with the increase of elevation. The identification results were validated by a field survey and unmanned aerial vehicle data. Progress has been made in the transition identification for the ecosystem states along the elevation gradient in mountainous areas by combining the remotely-sensed index with a potential analysis. This study also provided a reference for obtaining the elevation of the alpine tree line quickly and accurately. [ABSTRACT FROM AUTHOR]

The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future. [ABSTRACT FROM AUTHOR]

Recent increases in urbanization and tourism threaten the viability of UNESCO world heritage sites across the globe. The Angkor world heritage site located in southern Cambodia is now facing such a challenge. Over the past two decades, Angkor has seen over 300,000% growth in international tourist arrivals, which has led to uncontrolled development of the nearby city of Siem Reap. This study uses remote sensing and GIS to comprehend the process of urban expansion during the past 14 years, and has applied the CA-Markov model to predict future urban expansion. This paper analyzes the urban pressure on the Angkor site at different scales. The results reveal that the urban area of Siem Reap city increased from 28.23 km2 in 2004 to 73.56 km2 in 2017, an increase of 160%. Urban growth mainly represented a transit-oriented pattern of expansion, and it was also observed that land surfaces, such as arable land, forests, and grasslands, were transformed into urban residential land. The total constructed land area in the core and buffer zones increased by 12.99 km2 from 2004 to 2017, and 72% of the total increase was in the buffer zone. It is predicted that the built-up area in Siem Reap is expected to cover 135.09 km2 by 2025 and 159.14 km2 by 2030. The number of monuments that are most likely be affected by urban expansion is expected to increase from 9 in 2017 to 14 in 2025 and 17 in 2030. The urban area in Siem Reap has increased dramatically over the past decade and monuments continue to be decimated by urban expansion. This paper urges closer attention and urgent actions to minimize the urban pressure on the Angkor site in the future. [ABSTRACT FROM AUTHOR]

Google Earth (GE), a large Earth-observation data-based geographical information computer application, is an intuitive three-dimensional virtual globe. It enables archaeologists around the world to communicate and share their multisource data and research findings. Different from traditional geographical information systems (GIS), GE is free and easy to use in data collection, exploration, and visualization. In the past decade, many peer-reviewed articles on the use of GE in the archaeological cultural heritage (ACH) research field have been published. Most of these concern specific ACH investigations with a wide spatial coverage. GE can often be used to survey and document ACH so that both skilled archaeologists and the public can more easily and intuitively understand the results. Based on geographical tools and multi-temporal very high-resolution (VHR) satellite imagery, GE has been shown to provide spatio-temporal change information that has a bearing on the physical, environmental, and geographical character of ACH. In this review, in order to discuss the huge potential of GE, a comprehensive review of GE and its applications to ACH in the published scientific literature is first presented; case studies in five main research fields demonstrating how GE can be deployed as a key tool for studying ACH are then described. The selected case studies illustrate how GE can be used effectively to investigate ACH at multiple scales, discover new archaeological sites in remote regions, monitor historical sites, and assess damage in areas of conflict, and promote virtual tourism. These examples form the basis for highlighting current trends in remote sensing archaeology based on the GE platform, which could provide access to a low-cost and easy-to-use tool for communicating and sharing ACH geospatial data more effectively to the general public in the era of Digital Earth. Finally, a discussion of the merits and limitations of GE is presented along with conclusions and remaining challenges. [ABSTRACT FROM AUTHOR]

This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data were processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by spectral separability index (SSI) indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolution GF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations. [ABSTRACT FROM AUTHOR]