Remote Sensing Technologies and Applications in Urban Environments VIII最新文献

Research on the expansion of Beijing-Tianjin-Hebei China's capital metropolitan agglomeration based on night lighting technology 基于夜间照明技术的京津冀城市群扩展研究

Remote Sensing Technologies and Applications in Urban Environments VIII Pub Date : 2023-10-20 DOI: 10.1117/12.2676134

Xu Zhang, Josep Roca Cladera, B. Arellano Ramos

{"title":"Research on the expansion of Beijing-Tianjin-Hebei China's capital metropolitan agglomeration based on night lighting technology","authors":"Xu Zhang, Josep Roca Cladera, B. Arellano Ramos","doi":"10.1117/12.2676134","DOIUrl":"https://doi.org/10.1117/12.2676134","url":null,"abstract":"With the development of cities, it is an inevitable trend for cities to spread and expand to peripheral areas. Accurate measurement of urban boundaries and scope, so as to be able to obtain accurate results and laws of urban area expansion and the evolution characteristics of urban forms, is of great significance to the sustainable development of urban science. This paper attempts to use the method of yearly calibration, taking Beijing-Tianjin-Hebei China's capital economic circle as an example, to calibrate and sort out the multivariate and long-term night light remote sensing data of DMSP and VIIRS from 1992 to 2020, so that it can be applied to long-term evolution analysis and analysis and research. In addition, this paper also uses this data set to analyze the evolution pattern of urban expansion in the region and finds that cities in the Beijing-Tianjin-Hebei region mainly develop to the east and south, and the development is most obvious in the coastal areas. Urban sprawl in the region is accompanied by accelerated land area growth, low population growth, and low-density increases.","PeriodicalId":120667,"journal":{"name":"Remote Sensing Technologies and Applications in Urban Environments VIII","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126874933","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: 1

Harvesting remote sensing observations for quantifying burned area and built-up losses from the 2021 wildfires in Greece 收集遥感观测数据，用于量化2021年希腊野火的燃烧面积和累积损失

Remote Sensing Technologies and Applications in Urban Environments VIII Pub Date : 2023-10-20 DOI: 10.1117/12.2680887

Alexandros Paganias, C. Pappas

{"title":"Harvesting remote sensing observations for quantifying burned area and built-up losses from the 2021 wildfires in Greece","authors":"Alexandros Paganias, C. Pappas","doi":"10.1117/12.2680887","DOIUrl":"https://doi.org/10.1117/12.2680887","url":null,"abstract":"Mediterranean regions are heavily exposed to wildfires that can result in devastating casualties and infrastructure damage. Greece has been particularly affected by wildfires during recent years and the accurate mapping of the fire-exposed areas is essential. This can enhance our process understanding on such natural hazards, also supporting practitioners and decision-makers. Here, we combined remote sensing observations from the Copernicus Sentinel-2A satellite with GIS techniques to delineate the spatial extent and built-up losses at three example locations over Greece that were substantially affected by the summer 2021 wildfires, namely the regions of Northern Evia, Eastern Attica, and Achaia. The overall burned areas, as quantified with the pre- and post-fire Normalized Burn Ratio (i.e., dNBR), ranged from about 3 km2 to more than 500 km2 , while the exposed built-up features (buildings, roads, etc.) vary between the study regions following site-specific characteristics (built-up density, urban/rural interface, topography, etc.). The combination of publicly available remote sensing Earth observations and GIS techniques allowed us to obtain quantitative insights on the urban features exposed to these wildfires and their variability between the examined locations.","PeriodicalId":120667,"journal":{"name":"Remote Sensing Technologies and Applications in Urban Environments VIII","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125451849","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

Unsupervised vehicle extraction of bounding boxes in UAV images 无人机图像边界框的无监督车辆提取

Remote Sensing Technologies and Applications in Urban Environments VIII Pub Date : 2023-10-20 DOI: 10.1117/12.2680067

J. Yeom, Youkyung Han

引用次数: 0

Dual attention-based deep learning approach for building segmentation of remote sensing images 基于双注意力的深度学习方法在遥感图像分割中的应用

Remote Sensing Technologies and Applications in Urban Environments VIII Pub Date : 2023-10-20 DOI: 10.1117/12.2684330

N. Srivastava, K. Jain

{"title":"Dual attention-based deep learning approach for building segmentation of remote sensing images","authors":"N. Srivastava, K. Jain","doi":"10.1117/12.2684330","DOIUrl":"https://doi.org/10.1117/12.2684330","url":null,"abstract":"Accurate segmentation of buildings in remote sensing images is crucial for various applications, like urban planning, disaster management, and environmental monitoring. Traditional methods often struggle to handle the complexity of building structures and appearances. In this work, we utilize a multi-level multiple attention-based approach in the DeepLabv3+ model for obtaining global context and local information through the dual attention mechanism and convolutional block attention module. Rather than deploying superficial convolution layers, EfficientNetB7 is used as an encoder. Dual attention comprising of position attention module and channel attention module are added to the output of atrous spatial pyramid pooling model. This is done to obtain the inter-relationship between spatial and channel dimensions. The position attention module obtains the interdependencies of similar features irrespective of their distances through a weighted sum of the features at all positions in the image. Whereas channel attention focuses on improvising correlated channel information by incorporating relevant features across all channel maps. Also, convolutional block attention module is incorporated for better representation of low-level features which is added to the top of the pre-trained residual network backbone. The result of the two attention modules provides better segmentation results. The proposed model was executed on a building dataset, namely Massachusetts Building Dataset. The experimental results demonstrate the improved performance of the proposed model by increasing the mIoU by 0.47% on the dataset, respectively as compared to current state-of-the-art models.","PeriodicalId":120667,"journal":{"name":"Remote Sensing Technologies and Applications in Urban Environments VIII","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513447","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