The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences最新文献

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Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization 用于航空图像语义分割和矢量化的多传感器数据分析
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-291-2024
V. Knyaz, V. Kniaz, S. Zheltov, Kirill S. Petrov
{"title":"Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization","authors":"V. Knyaz, V. Kniaz, S. Zheltov, Kirill S. Petrov","doi":"10.5194/isprs-archives-xlviii-1-2024-291-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-291-2024","url":null,"abstract":"Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Visual Reinforcement Learning for Dynamic Object Detection 用于动态物体检测的视觉强化学习
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-679-2024
Xiangsheng Wang, Xikun Hu, Ping Zhong
{"title":"Visual Reinforcement Learning for Dynamic Object Detection","authors":"Xiangsheng Wang, Xikun Hu, Ping Zhong","doi":"10.5194/isprs-archives-xlviii-1-2024-679-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-679-2024","url":null,"abstract":"Abstract. Object detection is a widely studied task in computer vision. Current methods often focus on images captured from appropriate viewpoints. However, there is a large disparity between objects observed from different viewpoints in the real world. Dynamic Object Detection (DOD) method automatically adjusts the camera viewpoint in a visual scene to sequentially find optimal viewpoints. Currently, the DOD tasks are usually modeled as a sequential decision-making problem and solved using reinforcement learning methods. Existing approaches face challenges with sparse rewards and training instability. To tackle these issues, we proposed a single-step reward function and a lightweight network, respectively. The single-step reward function, which provides timely feedback, gives an efficient training process for DOD tasks. The lightweight network with few parameters can ensure the stability of the training process. To evaluate the effectiveness of our method, we developed a simulation dataset based on UE4, which consists of 1800 training images and 450 testing images. The dataset includes five object categories: vans, cars, trailers, box trucks and SUVs. Experiments demonstrate that our method outperforms SOTA object detectors on our simulation dataset. Specifically, the average precisions(APs) are improved from 89.1% to 96.0% when using the YOLOv8 object detector.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140991058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Heritage Building Information Modelling (HBIM): A Review of Published Case Studies 遗产建筑信息模型(HBIM):已发表案例研究综述
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-387-2024
Junshan Liu, Botao Li
{"title":"Heritage Building Information Modelling (HBIM): A Review of Published Case Studies","authors":"Junshan Liu, Botao Li","doi":"10.5194/isprs-archives-xlviii-1-2024-387-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-387-2024","url":null,"abstract":"Abstract. This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140993874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Standard PSInSAR approach: Experimental study on the sensitivity of different bounds change on the deformation 标准 PSInSAR 方法:不同边界变化对变形敏感性的实验研究
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-153-2024
Mostafa Ewais, T. Balz
{"title":"Standard PSInSAR approach: Experimental study on the sensitivity of different bounds change on the deformation","authors":"Mostafa Ewais, T. Balz","doi":"10.5194/isprs-archives-xlviii-1-2024-153-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-153-2024","url":null,"abstract":"Abstract. An experimental study conducted on standard PSInSAR technique to check the sensitivity of different bounds on the deformation results. This study is motivated by the results obtained from employment of different parameter ranges, with the assumption of the linear behavior of the deformation. despite implementation of slight parameter change shows similarity in most of the PS points. However, significant PS points display slightly different deformation measurements and trends. This change in the PS points motivated an experimental study to evaluate, analyze and assess the results from each different bounds through searching the solution space to find the best fitting to the estimated model, besides assessing of the temporal coherence estimator as quality and fitting indicator. A high-resolution TerraSAR-X (TSX) dataset is used to estimate the deformation using linear model and check the behavior over time. These dataset cover Wuhan city as a case study within two years to avoid non-linearity in the deformation. The main part of this experiment is the descriptive of the statistics across all trials of different bounds. Then, assessing the measurement results of the trials, and the coherence estimator that used as a quality indicator to assess the quality of the PS points in comparison with the standard deviation. The results of the measurements indicate that while the coherence estimator select the PS points based on their stability and fitting to estimated model but does not provide a big difference as function of different bounds through searching the solution space. The results are confirmed with the available global Positioning system (GPS) station in our study area and displays similar trends and patterns over all the trials with slight differences with the different parameters range. The statistical analysis and assessment reveals differences in the velocity results and erroneous during changing the parameters range.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping PRISMA 高光谱卫星图像在地方气候区绘图中的应用
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-643-2024
A. Vavassori, D. Oxoli, G. Venuti, M. Brovelli, Ali Badr Eldin Ali Mohamed, Afshin Moazzam, M. Siciliani de Cumis, P. Sacco, D. Tapete
{"title":"PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping","authors":"A. Vavassori, D. Oxoli, G. Venuti, M. Brovelli, Ali Badr Eldin Ali Mohamed, Afshin Moazzam, M. Siciliani de Cumis, P. Sacco, D. Tapete","doi":"10.5194/isprs-archives-xlviii-1-2024-643-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-643-2024","url":null,"abstract":"Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data 基于卫星遥感数据的露天回填矿山生态恢复工程监测与应用研究
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-365-2024
Shuneng Liang, Yang Li, Ning Zhang, Chenchao Xiao, Hongyan Wei
{"title":"Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data","authors":"Shuneng Liang, Yang Li, Ning Zhang, Chenchao Xiao, Hongyan Wei","doi":"10.5194/isprs-archives-xlviii-1-2024-365-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-365-2024","url":null,"abstract":"Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 106","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140993773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Assessing the Impact of the Türkiye February 2023 Earthquakes on Cultural Heritage Sites: A Multi-Disciplinary Approach Utilizing ARIA Maps and Social Media Collaboration 评估 2023 年 2 月土耳其地震对文化遗址的影响:利用 ARIA 地图和社交媒体协作的多学科方法
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-117-2024
Nusret Demir, Çiler Çilingiroğlu
{"title":"Assessing the Impact of the Türkiye February 2023 Earthquakes on Cultural Heritage Sites: A Multi-Disciplinary Approach Utilizing ARIA Maps and Social Media Collaboration","authors":"Nusret Demir, Çiler Çilingiroğlu","doi":"10.5194/isprs-archives-xlviii-1-2024-117-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-117-2024","url":null,"abstract":"Abstract. The earthquakes that occurred in our country on February 6, 2023, significantly damaged and destroyed cultural and historical assets. The Habib-i Nejjar Mosque in Antakya and other important historical sites suffered significant damage. This study provides a comprehensive account of the post-earthquake efforts carried out by volunteers from various disciplines, including archaeology, cartography, space sciences, and architecture, using social media platforms. The volunteers created an extensive database for cultural and archaeological sites in all 11 provinces that were declared disaster zones. This database made it easier to conduct a thorough evaluation of the damage. The database was augmented with surface displacement data, enabling an analysis of damage levels with the exactitude facilitated by the gathered information. The utilization of the Advanced Rapid Imaging and Analysis (ARIA) maps was pivotal in this study. The maps, created through partnerships between NASA and different space agencies, offer intricate satellite imagery and analysis of surface displacement. This is essential for evaluating the effects of earthquakes on cultural heritage sites. The utilization of ARIA maps in this study facilitated an accurate assessment of the seismic impact on cultural and historical resources. Additionally, Sentinel 1 images are used to generate displacement maps with the use of the LICSAR tool and the SBAS method. The study examined the state of about 1500 cultural heritage sites in 11 provinces in the aftermath of the earthquake. The report incorporated data from media coverage and input from relevant parties present at the scene, providing a thorough assessment of the situation following the disaster. For instance, the research revealed substantial shifts at prominent locations, like the UNESCO World Heritage site of Arslantepe in Malatya. This site experienced a displacement of over one meter towards the south and 75 cm towards the west, along with a subsidence of approximately 10 cm in the surrounding area. Furthermore, the study presented a visual representation that depicted the quantity of impaired cultural heritage sites in every province, providing a comprehensive evaluation of the impacted cultural and archaeological resources. This comprehensive strategy not only emphasized the magnitude of harm to cultural heritage but also emphasized the significance of interdisciplinary cooperation in disaster response and heritage preservation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
UAV and Deep Learning: Detection of selected riparian species along the Ganga River 无人机和深度学习:探测恒河沿岸的选定河岸物种
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-637-2024
Ravindra Nath Tripathi, Aishwarya Ramachandran, Karan Agarwal, Vikas Tripathi, R. Badola, Syed Ainul Hussain
{"title":"UAV and Deep Learning: Detection of selected riparian species along the Ganga River","authors":"Ravindra Nath Tripathi, Aishwarya Ramachandran, Karan Agarwal, Vikas Tripathi, R. Badola, Syed Ainul Hussain","doi":"10.5194/isprs-archives-xlviii-1-2024-637-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-637-2024","url":null,"abstract":"Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation 迈向可持续城市能源:用于太阳能潜能估计的强大深度学习框架
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-371-2024
Weiyan Lin, Jiasong Zhu, Yuansheng Hua, Qingyu Li, Lichao Mou, Xiao Xiang Zhu
{"title":"Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation","authors":"Weiyan Lin, Jiasong Zhu, Yuansheng Hua, Qingyu Li, Lichao Mou, Xiao Xiang Zhu","doi":"10.5194/isprs-archives-xlviii-1-2024-371-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-371-2024","url":null,"abstract":"Abstract. Rooftop photovoltaic is considered as a cost-effective and environmentally friendly solution to energy challenges in urban areas. To ensure photovoltaic efficiency, it is essential to accurately estimate rooftop solar potential and deploy solar panels wisely. During the past few years, deep learning-based estimation methods have emerged and mainly rely on inferring rooftop orientations from aerial imagery. However, we note that rooftops often appear diversely when images are taken at different solar azimuths, and this can lead to orientation misclassification. To address this, we propose a robust solar potential estimation framework, mainly composed of a rooftop orientation prediction network and a bilateral solar potential estimation module. Specifically, we first classify rooftops into five orientations, i.e., east, west, south, north towards, and flat with a semantic segmentation network. Afterward, opposing orientations are merged to alleviate misclassification caused by variant data acquisition time. Eventually, we compute solar potentials based on PVGIS and a weighting scheme. Experimental results on the RID dataset demonstrate the effectiveness of our approach in improving the accuracy of solar energy estimation.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140991536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors 基于多源传感器的西藏地表水和地下水时空耦合分析
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Pub Date : 2024-05-10 DOI: 10.5194/isprs-archives-xlviii-1-2024-663-2024
Mengran Wang, Jiaqi Yao, Fan Mo, Nan Xu
{"title":"Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors","authors":"Mengran Wang, Jiaqi Yao, Fan Mo, Nan Xu","doi":"10.5194/isprs-archives-xlviii-1-2024-663-2024","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-1-2024-663-2024","url":null,"abstract":"Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.\u0000","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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