Pub Date : 2025-01-10DOI: 10.1016/j.jag.2025.104354
He Ren, Zhen Yang, Fashuai Li, Maoxin Zhang, Yuwei Chen, Tingting He
Water-based photovoltaics (WPV) have emerged as a promising solution to land-use conflicts associated with solar photovoltaic systems. Accurate monitoring of the spatiotemporal distribution of WPV is essential for evaluating its development potential, environmental impacts, and informing policy decisions. Satellite remote sensing data offer a feasible approach for WPV mapping and monitoring. However, conventional image classification and deep learning methods often limited by sample size requirements, computational costs, and technical complexity, which hinder their widespread applicability. To address these challenges, this study proposes a novel index, the normalized difference photovoltaic index (NDPI), for WPV detection. We generated a global WPV map for the year 2023 using Sentinel-2 MSI imagery and NDPI. Additionally, by integrating NDPI with Landsat time series data, we determined the installation dates of WPV systems and evaluated their development trends from 2000 to 2023. Our results show that: (i) The NDPI demonstrated excellent performance in WPV detection, with overall accuracy for spatial location and installation dates of WPV was 0.935 and 0.927, respectively, and Kappa coefficients of 0.870 and 0.921. (ii) Global WPV coverage in 2023 reached 589.17 km2, with Asia being the primary contributor, accounting for over 97 %. China emerged as the leading country, with a WPV area of 472.92 km2, significantly exceeding other nations (< 50 km2). (iii) WPV experienced significant growth from 2000 to 2023, particularly after 2015. The increase in WPV area (434.57 km2) from 2015 to 2023 was nearly three times the total area covered in the previous 15 years. The proposed NDPI provides a universal approach for global WPV spatiotemporal monitoring and the update of basic information. It also provides potential for assessing the environmental impacts of WPV across its operational lifecycle.
{"title":"Satellite images reveal rapid development of global water-based photovoltaic over the past 20 years","authors":"He Ren, Zhen Yang, Fashuai Li, Maoxin Zhang, Yuwei Chen, Tingting He","doi":"10.1016/j.jag.2025.104354","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104354","url":null,"abstract":"Water-based photovoltaics (WPV) have emerged as a promising solution to land-use conflicts associated with solar photovoltaic systems. Accurate monitoring of the spatiotemporal distribution of WPV is essential for evaluating its development potential, environmental impacts, and informing policy decisions. Satellite remote sensing data offer a feasible approach for WPV mapping and monitoring. However, conventional image classification and deep learning methods often limited by sample size requirements, computational costs, and technical complexity, which hinder their widespread applicability. To address these challenges, this study proposes a novel index, the normalized difference photovoltaic index (NDPI), for WPV detection. We generated a global WPV map for the year 2023 using Sentinel-2 MSI imagery and NDPI. Additionally, by integrating NDPI with Landsat time series data, we determined the installation dates of WPV systems and evaluated their development trends from 2000 to 2023. Our results show that: (i) The NDPI demonstrated excellent performance in WPV detection, with overall accuracy for spatial location and installation dates of WPV was 0.935 and 0.927, respectively, and Kappa coefficients of 0.870 and 0.921. (ii) Global WPV coverage in 2023 reached 589.17 km<ce:sup loc=\"post\">2</ce:sup>, with Asia being the primary contributor, accounting for over 97 %. China emerged as the leading country, with a WPV area of 472.92 km<ce:sup loc=\"post\">2</ce:sup>, significantly exceeding other nations (< 50 km<ce:sup loc=\"post\">2</ce:sup>). (iii) WPV experienced significant growth from 2000 to 2023, particularly after 2015. The increase in WPV area (434.57 km<ce:sup loc=\"post\">2</ce:sup>) from 2015 to 2023 was nearly three times the total area covered in the previous 15 years. The proposed NDPI provides a universal approach for global WPV spatiotemporal monitoring and the update of basic information. It also provides potential for assessing the environmental impacts of WPV across its operational lifecycle.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"36 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1016/j.jag.2024.104349
Xuanguang Liu, Yujie Li, Chenguang Dai, Zhenchao Zhang, Lei Ding, Mengmeng Li, Hanyun Wang
Building extraction from very high-resolution remote-sensing images still faces two main issues: (1) small buildings are severely omitted and the extracted building shapes have a low consistency with ground truths. (2) supervised deep-learning methods have poor performance in few-shot scenarios, limiting the practical application of these methods. To address the first issue, we propose an asymmetric Siamese multitask network integrating adversarial edge learning called ASMBR-Net for building extraction. It contains an efficient asymmetric Siamese feature extractor comprising pre-trained backbones of convolutional neural networks and Transformers under pre-training and fine-tuning paradigms. This extractor balances the local and global feature representation and reduces training costs. Adversarial edge-learning technology automatically integrates edge constraints and strengthens the modeling ability of small and complex building-shaped patterns. Aiming to overcome the second issue, we introduce a self-training framework and design an instance transfer strategy to generate reliable pseudo-samples. We examined the proposed method on the WHU and Massachusetts (MA) datasets and a self-constructed Dongying (DY) dataset, comparing it with state-of-the-art methods. The experimental results show that our method achieves the highest F1-score of 96.06%, 86.90%, and 84.98% on the WHU, MA, and DY datasets, respectively. Ablation experiments further verify the effectiveness of the proposed method. The code is available at: https://github.com/liuxuanguang/ASMBR-Net
从极高分辨率遥感图像中提取建筑物仍然面临两个主要问题：（1）小型建筑物被严重遗漏，提取的建筑物形状与地面实况的一致性较低。(2) 有监督的深度学习方法在少镜头场景下性能较差，限制了这些方法的实际应用。针对第一个问题，我们提出了一种集成对抗边缘学习的非对称连体多任务网络，称为 ASMBR-Net，用于建筑物提取。它包含一个高效的非对称连体特征提取器，由预先训练的卷积神经网络骨干和预训练和微调范式下的变形器组成。这种提取器平衡了局部和全局特征表示，降低了训练成本。对抗边缘学习技术可自动整合边缘约束，增强对小型和复杂建筑形态的建模能力。为了克服第二个问题，我们引入了一个自我训练框架，并设计了一种实例转移策略来生成可靠的伪样本。我们在 WHU 和 Massachusetts（MA）数据集以及自建的东营（DY）数据集上检验了所提出的方法，并将其与最先进的方法进行了比较。实验结果表明，我们的方法在 WHU、MA 和 DY 数据集上分别取得了 96.06%、86.90% 和 84.98% 的最高 F1 分数。消融实验进一步验证了所提方法的有效性。代码见： https://github.com/liuxuanguang/ASMBR-Net
{"title":"Extraction buildings from very high-resolution images with asymmetric siamese multitask networks and adversarial edge learning","authors":"Xuanguang Liu, Yujie Li, Chenguang Dai, Zhenchao Zhang, Lei Ding, Mengmeng Li, Hanyun Wang","doi":"10.1016/j.jag.2024.104349","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104349","url":null,"abstract":"Building extraction from very high-resolution remote-sensing images still faces two main issues: (1) small buildings are severely omitted and the extracted building shapes have a low consistency with ground truths. (2) supervised deep-learning methods have poor performance in few-shot scenarios, limiting the practical application of these methods. To address the first issue, we propose an asymmetric Siamese multitask network integrating adversarial edge learning called ASMBR-Net for building extraction. It contains an efficient asymmetric Siamese feature extractor comprising pre-trained backbones of convolutional neural networks and Transformers under pre-training and fine-tuning paradigms. This extractor balances the local and global feature representation and reduces training costs. Adversarial edge-learning technology automatically integrates edge constraints and strengthens the modeling ability of small and complex building-shaped patterns. Aiming to overcome the second issue, we introduce a self-training framework and design an instance transfer strategy to generate reliable pseudo-samples. We examined the proposed method on the WHU and Massachusetts (MA) datasets and a self-constructed Dongying (DY) dataset, comparing it with state-of-the-art methods. The experimental results show that our method achieves the highest F1-score of 96.06%, 86.90%, and 84.98% on the WHU, MA, and DY datasets, respectively. Ablation experiments further verify the effectiveness of the proposed method. The code is available at: <ce:inter-ref xlink:href=\"https://github.com/liuxuanguang/ASMBR-Net\" xlink:type=\"simple\">https://github.com/liuxuanguang/ASMBR-Net</ce:inter-ref>","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"82 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1016/j.jag.2025.104359
Hadi H. Jaafar, Lara H. Sujud
Accurate evapotranspiration (ET) estimation is crucial for optimizing irrigation and managing water resources at the field scale. This study investigates the potential of unmanned aerial vehicles (UAVs) equipped with the MicaSense Altum sensor for high-resolution ET mapping using the Hybrid Single Source Energy Balance (HSEB) model. We focused on a 4.5 ha sprinkle-irrigated potato field at the American University of Beirut Agricultural Research and Education Center (AREC) in Lebanon’s Bekaa Valley. Eleven UAV flights were conducted throughout the growing season, synchronized with Landsat 8 and 9, and MODIS LST overpasses. HSEB ET from the Altum sensor was compared against EC data from a flux tower setup, and a comparative analysis was performed with HSEB ET from Landsat 8, Landsat 9, and Sentinel-2 (with MODIS LST). HSEB ET from the UAV exhibited very close agreement (3 % lower) with EC data, with a low RMSE of 0.60 mm/day. Notably, UAV-derived land surface temperature (LST) was on average 3 % higher than infrared radiometer LST. In contrast, comparisons of UAV LST with Landsat and S2MOD LST data revealed significant overestimations of LST (43 % and 24 %, respectively). Consequently, HSEB ET from Landsat and S2MOD were lower than EC ET by 17 % and 6 %, respectively. The strong agreement between UAV-HSEB and EC data underscores the potential of UAV thermal data for accurate irrigation management in heterogeneous fields using the HSEB model. While limitations exist regarding coverage area and cost, the detailed information obtained from UAVs can be highly valuable for optimizing irrigation practices and improving water use efficiency at sub-field scales.
{"title":"High resolution evapotranspiration from UAV multispectral thermal imagery: Validation and comparison with EC, Landsat, and fused S2-MODIS HSEB ET","authors":"Hadi H. Jaafar, Lara H. Sujud","doi":"10.1016/j.jag.2025.104359","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104359","url":null,"abstract":"Accurate evapotranspiration (ET) estimation is crucial for optimizing irrigation and managing water resources at the field scale. This study investigates the potential of unmanned aerial vehicles (UAVs) equipped with the MicaSense Altum sensor for high-resolution ET mapping using the Hybrid Single Source Energy Balance (HSEB) model. We focused on a 4.5 ha sprinkle-irrigated potato field at the American University of Beirut Agricultural Research and Education Center (AREC) in Lebanon’s Bekaa Valley. Eleven UAV flights were conducted throughout the growing season, synchronized with Landsat 8 and 9, and MODIS LST overpasses. HSEB ET from the Altum sensor was compared against EC data from a flux tower setup, and a comparative analysis was performed with HSEB ET from Landsat 8, Landsat 9, and Sentinel-2 (with MODIS LST). HSEB ET from the UAV exhibited very close agreement (3 % lower) with EC data, with a low RMSE of 0.60 mm/day. Notably, UAV-derived land surface temperature (LST) was on average 3 % higher than infrared radiometer LST. In contrast, comparisons of UAV LST with Landsat and S2MOD LST data revealed significant overestimations of LST (43 % and 24 %, respectively). Consequently, HSEB ET from Landsat and S2MOD were lower than EC ET by 17 % and 6 %, respectively. The strong agreement between UAV-HSEB and EC data underscores the potential of UAV thermal data for accurate irrigation management in heterogeneous fields using the HSEB model. While limitations exist regarding coverage area and cost, the detailed information obtained from UAVs can be highly valuable for optimizing irrigation practices and improving water use efficiency at sub-field scales.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-05DOI: 10.1016/j.jag.2024.104352
Pengxiang Su, Yingwei Yan, Hao Li, Hangbin Wu, Chun Liu, Wei Huang
As global urbanization intensifies, conflicts between humans and urban infrastructure increasingly affect socio-economic and environmental sustainability. Recently, using image data and deep learning to investigate the interactions between humans and urban infrastructure has been a popular approach since the fast development of Artificial Intelligence (AI). However, the convergence of data fusion, deep learning, and human-urban infrastructure interaction studies remains underexplored. Here we systematically analyze 3,552 papers from 2013 to 2023 that use image data to investigate the intersection area of data fusion, deep learning, and human and urban infrastructure interactions, aiming to elucidate the relationships among these three key elements. We found that the cross-applications of deep learning in the papers reviewed are not standardized. Given the trend of diversified data fusion, data fusion about real-world dynamic interactions is scarce. Lastly, four potential future research directions are identified: (1) understanding the dynamic and complex interaction processes; (2) exploring the potential and standards for the application of deep learning; (3) focusing more on research concerning cities in the Global South; (4) establishing suitable training datasets for the interaction between urban infrastructures and humans, which may provide valuable insights for applying foundation models in future urban studies.
{"title":"Images and deep learning in human and urban infrastructure interactions pertinent to sustainable urban studies: Review and perspective","authors":"Pengxiang Su, Yingwei Yan, Hao Li, Hangbin Wu, Chun Liu, Wei Huang","doi":"10.1016/j.jag.2024.104352","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104352","url":null,"abstract":"As global urbanization intensifies, conflicts between humans and urban infrastructure increasingly affect socio-economic and environmental sustainability. Recently, using image data and deep learning to investigate the interactions between humans and urban infrastructure has been a popular approach since the fast development of Artificial Intelligence (AI). However, the convergence of data fusion, deep learning, and human-urban infrastructure interaction studies remains underexplored. Here we systematically analyze 3,552 papers from 2013 to 2023 that use image data to investigate the intersection area of data fusion, deep learning, and human and urban infrastructure interactions, aiming to elucidate the relationships among these three key elements. We found that the cross-applications of deep learning in the papers reviewed are not standardized. Given the trend of diversified data fusion, data fusion about real-world dynamic interactions is scarce. Lastly, four potential future research directions are identified: (1) understanding the dynamic and complex interaction processes; (2) exploring the potential and standards for the application of deep learning; (3) focusing more on research concerning cities in the Global South; (4) establishing suitable training datasets for the interaction between urban infrastructures and humans, which may provide valuable insights for applying foundation models in future urban studies.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"130 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects AOI’s expiration in the real world. They fail to fulfill the precision requirements of Mobile Internet Online-to-Offline (O2O) businesses. These businesses require AOI boundary accuracy down to a specific community, school, or hospital. In this paper, we propose a fully end-to-end multimodal AOI TRansformer (AOITR) model designed for simultaneously detecting accurate AOI boundaries and validating AOI’s reliability by leveraging remote sensing imagery coupled with geographical prior. Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification. Instead, our AOITR begins by selecting a point-of-interest (POI) of specific category, which can be easily obtained via web crawler, and uses it to retrieve corresponding remote sensing imagery and geographical prior such as entrance POIs and road nodes. This information helps to build a multimodal detection model based on transformer encoder-decoder architecture to regress the accurate AOI polygon. Additionally, we utilize the dynamic features from human mobility, nearby POIs, and logistics addresses for AOI reliability evaluation via a cascaded network module. The experimental results reveal that our algorithm achieves a significant improvement on Intersection over Union (IoU) metric, surpassing previous methods by a large margin. Furthermore, the AOIs produced by AOITR have substantially enriched our AOI library and have been successfully applied on over 10 different O2O scenarios including Alipay’s face scan payment service.
{"title":"Multimodal urban areas of interest generation via remote sensing imagery and geographical prior","authors":"Chuanji Shi, Yingying Zhang, Jiaotuan Wang, Xin Guo, Qiqi Zhu","doi":"10.1016/j.jag.2024.104326","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104326","url":null,"abstract":"Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects AOI’s expiration in the real world. They fail to fulfill the precision requirements of Mobile Internet Online-to-Offline (O2O) businesses. These businesses require AOI boundary accuracy down to a specific community, school, or hospital. In this paper, we propose a fully end-to-end multimodal AOI TRansformer (AOITR) model designed for simultaneously detecting accurate AOI boundaries and validating AOI’s reliability by leveraging remote sensing imagery coupled with geographical prior. Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification. Instead, our AOITR begins by selecting a point-of-interest (POI) of specific category, which can be easily obtained via web crawler, and uses it to retrieve corresponding remote sensing imagery and geographical prior such as entrance POIs and road nodes. This information helps to build a multimodal detection model based on transformer encoder-decoder architecture to regress the accurate AOI polygon. Additionally, we utilize the dynamic features from human mobility, nearby POIs, and logistics addresses for AOI reliability evaluation via a cascaded network module. The experimental results reveal that our algorithm achieves a significant improvement on Intersection over Union (IoU) metric, surpassing previous methods by a large margin. Furthermore, the AOIs produced by AOITR have substantially enriched our AOI library and have been successfully applied on over 10 different O2O scenarios including Alipay’s face scan payment service.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"21 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.jag.2024.104353
Yuan Tao, Wanzeng Liu, Jun Chen, Jingxiang Gao, Ran Li, Xinpeng Wang, Ye Zhang, Jiaxin Ren, Shunxi Yin, Xiuli Zhu, Tingting Zhao, Xi Zhai, Yunlu Peng
Accurately mapping urban functional zone (UFZ) provides crucial foundational geographic information services for urban sustainable development, territorial spatial planning, and public resource allocation. UFZs are blocks within urban environments that serve specific functions, typically comprising physical objects with specific spatial distribution patterns and semantic objects of various types. However, previous studies for identifying UFZs have focused on physical or semantic aspects of UFZs, overlooking the spatial relationships and connectivity among objects. Furthermore, few have leveraged the constructed graphs by heterogeneous geospatial data to identify functional zones by street block-based mapping units. To bridge this gap, we developed a graph-based multimodal data fusion framework (G2MF) to identify UFZs. It is a fully graph-based identification framework with a feature-level fusion strategy that integrates very high-resolution remote sensing images and point of interest data. Firstly, physical objects within a UFZ unit are classified using semantic segmentation technology; then, the two independent graph structures are constructed for both physical and semantic objects within the UFZ unit; finally, the graphs are input into the proposed graph-based multimodal fusion network for UFZ identification. Experimental results show that the proposed G2MF achieves an overall identification accuracy of 88.5 % on test data from four Chinese cities and also exhibits good generalization ability on test data with geographic isolation. This study not only promotes the development of automatic UFZ identification technology but also provides new directions and methodologies for future urban big data analysis. Our source codes are released at https://github.com/yuantaogiser/G2MF.
{"title":"A graph-based multimodal data fusion framework for identifying urban functional zone","authors":"Yuan Tao, Wanzeng Liu, Jun Chen, Jingxiang Gao, Ran Li, Xinpeng Wang, Ye Zhang, Jiaxin Ren, Shunxi Yin, Xiuli Zhu, Tingting Zhao, Xi Zhai, Yunlu Peng","doi":"10.1016/j.jag.2024.104353","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104353","url":null,"abstract":"Accurately mapping urban functional zone (UFZ) provides crucial foundational geographic information services for urban sustainable development, territorial spatial planning, and public resource allocation. UFZs are blocks within urban environments that serve specific functions, typically comprising physical objects with specific spatial distribution patterns and semantic objects of various types. However, previous studies for identifying UFZs have focused on physical or semantic aspects of UFZs, overlooking the spatial relationships and connectivity among objects. Furthermore, few have leveraged the constructed graphs by heterogeneous geospatial data to identify functional zones by street block-based mapping units. To bridge this gap, we developed a graph-based multimodal data fusion framework (G2MF) to identify UFZs. It is a fully graph-based identification framework with a feature-level fusion strategy that integrates very high-resolution remote sensing images and point of interest data. Firstly, physical objects within a UFZ unit are classified using semantic segmentation technology; then, the two independent graph structures are constructed for both physical and semantic objects within the UFZ unit; finally, the graphs are input into the proposed graph-based multimodal fusion network for UFZ identification. Experimental results show that the proposed G2MF achieves an overall identification accuracy of 88.5 % on test data from four Chinese cities and also exhibits good generalization ability on test data with geographic isolation. This study not only promotes the development of automatic UFZ identification technology but also provides new directions and methodologies for future urban big data analysis. Our source codes are released at <ce:inter-ref xlink:href=\"https://github.com/yuantaogiser/G2MF\" xlink:type=\"simple\">https://github.com/yuantaogiser/G2MF</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"6 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of water resources in supporting food production is ever increasing, especially in the face of climate change, urbanization and population growth. This study aims to map and analyze the spatio-temporal dynamics of irrigated agricultural areas to support improved planning of irrigation water and irrigation sector in Nepal. Using the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) employing Google Earth Engine (GEE) platform, this study classifies and analyzes change in irrigated and rainfed areas over the past two decades. NDVI time series analysis across different physiographic regions uncovered two cropping cycles annually in the Terai and Siwalik regions. In contrast, predominantly a single cropping cycle was observed in the Middle and High Mountain regions. The k-means clustering algorithm was applied to NDVI time series within the agriculture land use database of the International Centre for Integrated Mountain Development (ICIMOD) for Nepal. The obtained irrigated areas distribution were also analyzed across different provinces of Nepal as provinces are the main functional administrative divisions after federal level that are responsible for irrigation development. The produced irrigation areas distribution showed reasonable accuracy as compared to the statistical irrigation areas database of the Department of Water Resources and Irrigation (DWRI), Nepal. The results showed that, on average, approximately 60% (2.18 million hectares) of agricultural land was irrigated annually over the past decade. The findings will provide valuable insights for sustainable irrigation and water resource management, crop productivity enhancement, and strategy formulation to ensure food and water security in Nepal.
{"title":"Mapping Spatio-Temporal dynamics of irrigated agriculture in Nepal using MODIS NDVI and statistical data with Google Earth Engine: A step towards improved irrigation planning","authors":"Pramit Ghimire, Saroj Karki, Vishnu Prasad Pandey, Ananta Man Singh Pradhan","doi":"10.1016/j.jag.2024.104345","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104345","url":null,"abstract":"The importance of water resources in supporting food production is ever increasing, especially in the face of climate change, urbanization and population growth. This study aims to map and analyze the spatio-temporal dynamics of irrigated agricultural areas to support improved planning of irrigation water and irrigation sector in Nepal. Using the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) employing Google Earth Engine (GEE) platform, this study classifies and analyzes change in irrigated and rainfed areas over the past two decades. NDVI time series analysis across different physiographic regions uncovered two cropping cycles annually in the Terai and Siwalik regions. In contrast, predominantly a single cropping cycle was observed in the Middle and High Mountain regions. The k-means clustering algorithm was applied to NDVI time series within the agriculture land use database of the International Centre for Integrated Mountain Development (ICIMOD) for Nepal. The obtained irrigated areas distribution were also analyzed across different provinces of Nepal as provinces are the main functional administrative divisions after federal level that are responsible for irrigation development. The produced irrigation areas distribution showed reasonable accuracy as compared to the statistical irrigation areas database of the Department of Water Resources and Irrigation (DWRI), Nepal. The results showed that, on average, approximately 60% (2.18 million hectares) of agricultural land was irrigated annually over the past decade. The findings will provide valuable insights for sustainable irrigation and water resource management, crop productivity enhancement, and strategy formulation to ensure food and water security in Nepal.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"125 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.jag.2024.104347
Bin Liu, Jian Kang, Haiyan Guan, Xiaodong Zhi, Yongtao Yu, Lingfei Ma, Daifeng Peng, Linlin Xu, Dongchuan Wang
Although real-time semantic segmentation of pavement cracks is crucial for road evaluation and maintenance decision-making, it is a challenging task due to low operational efficiency and over-segmentation of existing methods. To address these challenges, in this paper, incorporating Transformers and CNNs, we propose a real-time triple-branch crack semantic segmentation network (RTCNet) using digital camera images. The three branches include a detail branch for capturing local detail features, a context branch for extracting global contextual information, and a boundary branch for obtaining crack boundary information. First, to further enhance crack features, we design a Detail Enhance Transformer (DET) module for enlarging global receptive fields and a Multiscale Aggregation (MSA) module for multiscale learning in the context branch. Second, a Boundary Refinement (BR) module with Sobel operators embedded in the boundary branch is designed to refine the crack boundaries. Last, a Detail-Context Fusion (DCF) module is designed to aggregate the intermediate features extracted from the different branches efficiently Comprehensive quantitative and visual comparisons on four datasets showed that the proposed RTCNet outperforms the comparative models in terms of efficiency and effectiveness with the highest F1-score, mIoU, and Frames Per Second (FPS) of 90.56%, 90.25%, and 87.34 in DeepCrack537 dataset, respectively. We also contribute an extensive dataset of pavement cracks, consisting of 464 manually annotated digital images, which is publicly accessible at https://github.com/NJSkate/BeijingHighway-dataset.
{"title":"RTCNet: A novel real-time triple branch network for pavement crack semantic segmentation","authors":"Bin Liu, Jian Kang, Haiyan Guan, Xiaodong Zhi, Yongtao Yu, Lingfei Ma, Daifeng Peng, Linlin Xu, Dongchuan Wang","doi":"10.1016/j.jag.2024.104347","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104347","url":null,"abstract":"Although real-time semantic segmentation of pavement cracks is crucial for road evaluation and maintenance decision-making, it is a challenging task due to low operational efficiency and over-segmentation of existing methods. To address these challenges, in this paper, incorporating Transformers and CNNs, we propose a real-time triple-branch crack semantic segmentation network (RTCNet) using digital camera images. The three branches include a detail branch for capturing local detail features, a context branch for extracting global contextual information, and a boundary branch for obtaining crack boundary information. First, to further enhance crack features, we design a Detail Enhance Transformer (DET) module for enlarging global receptive fields and a Multiscale Aggregation (MSA) module for multiscale learning in the context branch. Second, a Boundary Refinement (BR) module with Sobel operators embedded in the boundary branch is designed to refine the crack boundaries. Last, a Detail-Context Fusion (DCF) module is designed to aggregate the intermediate features extracted from the different branches efficiently Comprehensive quantitative and visual comparisons on four datasets showed that the proposed RTCNet outperforms the comparative models in terms of efficiency and effectiveness with the highest F<ce:inf loc=\"post\">1</ce:inf>-score, mIoU, and Frames Per Second (FPS) of 90.56%, 90.25%, and 87.34 in DeepCrack537 dataset, respectively. We also contribute an extensive dataset of pavement cracks, consisting of 464 manually annotated digital images, which is publicly accessible at <ce:inter-ref xlink:href=\"https://github.com/NJSkate/BeijingHighway-dataset\" xlink:type=\"simple\">https://github.com/NJSkate/BeijingHighway-dataset</ce:inter-ref>.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"41 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for quantitatively assessing the MT-InSAR-derived deformation results. We first introduce an iterative seasonal trend decomposition using loess (STL) method to confirm the optimal period for separating seasonal components from the displacement time series. Then, an absolute differences detector with rolling windows is proposed to quantify the subsidence ratio within the time series and allow deformation hotspots to be more visible. To validate the effectiveness of the proposed method, 468 scenes of Sentinel-1A ascending images from Jun. 2015 to Nov. 2023 over the Hong Kong Mass Transit Railway (MTR) are adopted. The results indicate that 99.2% of areas are relatively stable with the displacement velocity ranging from −2 mm/year to 2 mm/year, and 84% of the study area remained a subsidence ratio below 0.3, except for localized hotspots that exhibited either short or long-term subsidence trends. The findings of this study indicate that multiple deformation hotspots were identified at the intersections of several metro lines in the Kowloon Peninsula and along the Island line. In addition to the displacement velocity from the conventional MT-InSAR, the overall and annual subsidence ratios have been demonstrated to be useful indicators for quantitative assessment of the construction-induced deformation.
{"title":"Spatiotemporal-grained quantitative assessment of construction-induced deformation along the MTR in Hong Kong using MT-InSAR and iterative STL-based subsidence ratio analysis","authors":"Jiayuan Zhang, Yuhao Liu, Bochen Zhang, Siting Xiong, Chisheng Wang, Songbo Wu, Wu Zhu","doi":"10.1016/j.jag.2024.104342","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104342","url":null,"abstract":"Multi-temporal synthetic aperture radar interferometry (MT-InSAR) offers unique advantages in monitoring ground deformation and structural stability along the metro lines. However, a vast number of complex deformation points, millions and even more, can be derived from InSAR making it challenging to identify the deformation hotspot in time series automatically. This paper proposes a novel method for quantitatively assessing the MT-InSAR-derived deformation results. We first introduce an iterative seasonal trend decomposition using loess (STL) method to confirm the optimal period for separating seasonal components from the displacement time series. Then, an absolute differences detector with rolling windows is proposed to quantify the subsidence ratio within the time series and allow deformation hotspots to be more visible. To validate the effectiveness of the proposed method, 468 scenes of Sentinel-1A ascending images from Jun. 2015 to Nov. 2023 over the Hong Kong Mass Transit Railway (MTR) are adopted. The results indicate that 99.2% of areas are relatively stable with the displacement velocity ranging from −2 mm/year to 2 mm/year, and 84% of the study area remained a subsidence ratio below 0.3, except for localized hotspots that exhibited either short or long-term subsidence trends. The findings of this study indicate that multiple deformation hotspots were identified at the intersections of several metro lines in the Kowloon Peninsula and along the Island line. In addition to the displacement velocity from the conventional MT-InSAR, the overall and annual subsidence ratios have been demonstrated to be useful indicators for quantitative assessment of the construction-induced deformation.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"27 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.jag.2024.104350
Chuanjun Wu, Peng Shen, Stefano Tebaldini, Mingsheng Liao, Lu Zhang
This paper introduces a method for estimating forest above-ground biomass (AGB) using the Interferometric SAR (InSAR)-based Phase Histogram (PH) technique. This novel technique allows for the extraction of 3D vertical forest structure using only a single interferometric pair to acquire a coarse-resolution backscatter intensity distribution in the height direction. Through 3D backscatter distribution, we can extract forest height, the intensity at predefined height bins and introduce the volume-to-ground intensity ratio (VGR) factor to investigate their sensitivities to forest AGB. To validate the method, we use the airborne fully polarized TomoSense dataset, flight-tested by European Space Agency (ESA) in Kermeter area at Eifel National Park, Germany, in 2020. We adopt both multivariate linear stepwise regression (MLSR) and random forest (RF) models to verify the feasibility of the PH technique in forest AGB estimation. Experimental results show that the PH technique effectively captures the vertical structure of the forest at a certain resolution. The forest height, the PH-derived backscatter intensity at a fixed height and VGR have good positive correlation with AGB. Notably, combining forest height, the intensity at fixed height layers and VGR significantly improves the inversion precision of forest AGB. Specifically, compared with LiDAR-derived AGB, the average root-mean-square error (RMSE) of MLSR and RF models estimates combining P- and L-band 2D + 3D observables are 57.92 ton/ha and 55.11 ton/ha, with Pearson correlation coefficient (PCC) of 0.75 and 0.77, respectively. This study presents a promising alternative approach for current and future SAR Earth observation missions aimed at forest vertical structure construction and AGB estimation when only a few of single-polarization SAR images are available.
本文介绍了一种基于干涉SAR （InSAR）的相位直方图（PH）技术估算森林地上生物量（AGB）的方法。这种新技术允许仅使用单个干涉对提取三维垂直森林结构，以获得在高度方向上的粗分辨率后向散射强度分布。通过三维后向散射分布，提取森林高度、预定义高度仓的强度，并引入体地强度比（VGR）因子，考察其对森林AGB的敏感性。为了验证该方法，我们使用了机载全极化TomoSense数据集，该数据集于2020年由欧洲航天局（ESA）在德国艾菲尔国家公园的Kermeter地区进行了飞行测试。采用多元线性逐步回归（MLSR）和随机森林（RF）模型验证PH技术在森林AGB估计中的可行性。实验结果表明，PH技术在一定分辨率下能有效地捕获森林的垂直结构。森林高度、固定高度ph反演后向散射强度和VGR与AGB呈良好的正相关关系。值得注意的是，结合森林高度、固定高程层强度和VGR显著提高了森林AGB的反演精度。与激光雷达AGB相比，结合P波段和l波段2D + 3D观测数据的MLSR和RF模型估计的平均均方根误差（RMSE）分别为57.92和55.11 t /ha， Pearson相关系数（PCC）分别为0.75和0.77。该研究为当前和未来针对森林垂直结构构建和AGB估算的SAR对地观测任务提供了一种有希望的替代方法。
{"title":"InSAR-based estimation of forest above-ground biomass using phase histogram technique","authors":"Chuanjun Wu, Peng Shen, Stefano Tebaldini, Mingsheng Liao, Lu Zhang","doi":"10.1016/j.jag.2024.104350","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104350","url":null,"abstract":"This paper introduces a method for estimating forest above-ground biomass (AGB) using the Interferometric SAR (InSAR)-based Phase Histogram (PH) technique. This novel technique allows for the extraction of 3D vertical forest structure using only a single interferometric pair to acquire a coarse-resolution backscatter intensity distribution in the height direction. Through 3D backscatter distribution, we can extract forest height, the intensity at predefined height bins and introduce the volume-to-ground intensity ratio (VGR) factor to investigate their sensitivities to forest AGB. To validate the method, we use the airborne fully polarized TomoSense dataset, flight-tested by European Space Agency (ESA) in Kermeter area at Eifel National Park, Germany, in 2020. We adopt both multivariate linear stepwise regression (MLSR) and random forest (RF) models to verify the feasibility of the PH technique in forest AGB estimation. Experimental results show that the PH technique effectively captures the vertical structure of the forest at a certain resolution. The forest height, the PH-derived backscatter intensity at a fixed height and VGR have good positive correlation with AGB. Notably, combining forest height, the intensity at fixed height layers and VGR significantly improves the inversion precision of forest AGB. Specifically, compared with LiDAR-derived AGB, the average root-mean-square error (RMSE) of MLSR and RF models estimates combining P- and L-band 2D + 3D observables are 57.92 ton/ha and 55.11 ton/ha, with Pearson correlation coefficient (PCC) of 0.75 and 0.77, respectively. This study presents a promising alternative approach for current and future SAR Earth observation missions aimed at forest vertical structure construction and AGB estimation when only a few of single-polarization SAR images are available.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"89 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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