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Journal articles on the topic 'Remote assessment of building'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Han, Xu, Jing Wang, Xun Liu, Jun Du, Xiaolan Bai, and Ran Ji. "PromptNet: Prompt Learning for Roof Photovoltaic Potential Assessment." Journal of Physics: Conference Series 2755, no. 1 (2024): 012042. http://dx.doi.org/10.1088/1742-6596/2755/1/012042.

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Abstract An increasing number of works have been proposed to use remote sensing images to assess the potential for rooftop Photovoltaic (PV) energy development in buildings. However, most methods focus mainly on the remote sensing images themselves, ignoring the key prior information of building type. Thus most works with Deeplabv3+ as backbone present suboptimal performance. To overcome this challenge, we propose a novel approach PromptNet that embeds the building types as prior knowledge and feed it into prompt learning for predict roof PV energy Potential. Specifically, a pre-trained semant

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Tang, Jiawei, Shengquan Yang, Shujuan Huang, and Bozhi Xiao. "Remote Sensing Building Damage Assessment Based on Machine Learning." International Journal of Advanced Network, Monitoring and Controls 9, no. 3 (2024): 1–12. http://dx.doi.org/10.2478/ijanmc-2024-0021.

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Abstract After the occurrence of various types of disasters, including natural disasters and man-made damage, aid workers need accurate and timely data, such as the damage status of buildings, in order to take effective measures for rescue. So as to solve this problem, this paper researches and designs a building damage classification system based on machine learning. The damage assessment system consists of two network models (building extraction network and damage classification network). This article analyzes and designs the structure of each network model, and discusses the principles rela

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Dong, Can, Wenyin Song, and Rui Liu. "Pixel-level Identification and Collapse of Post-earthquake Building Complexes Based on Satellite Remote Sensing Images." Advances in Engineering Technology Research 12, no. 1 (2024): 1337. https://doi.org/10.56028/aetr.12.1.1337.2024.

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Due to the obvious diversity and complexity of damage patterns, geometries, and spatial scales of urban building complex earthquake hazards, conventional identification and assessment methods are less generalizable in real post-earthquake scenarios. Compared with time-range signals such as kinetic acceleration, image/video data provide a new source of perceptual information for accurately assessing the post-earthquake damage of urban building complexes. In order to realize the integrated, comprehensive, and rapid identification and assessment of the structural damage of urban buildings after a

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Gonzalez-Drigo, Ramon, Esteban Cabrera, Guido Luzi, Luis Pujades, Yeudy Vargas-Alzate, and Jorge Avila-Haro. "Assessment of Post-Earthquake Damaged Building with Interferometric Real Aperture Radar." Remote Sensing 11, no. 23 (2019): 2830. http://dx.doi.org/10.3390/rs11232830.

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In this study the main results of a detailed analysis of an actual building, which was severely damaged during the Mw 5.1, May 11th 2011, Lorca earthquake (Murcia, Spain) are presented. The dynamic behavior of the building was analyzed by means of empirical and numerical approaches. The displacement response of the building submitted to ambient noise was recorded by using a Real Aperture Radar (RAR). This approach provides a secure remote sensing procedure that does not require entering the building. Based on the blueprints and other available graphical information about the building, a numeri

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Geiß, Christian, Hannes Taubenböck, Sergey Tyagunov, Anita Tisch, Joachim Post, and Tobia Lakes. "Assessment of Seismic Building Vulnerability from Space." Earthquake Spectra 30, no. 4 (2014): 1553–83. http://dx.doi.org/10.1193/121812eqs350m.

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This paper quantitatively evaluates the suitability of multi-sensor remote sensing to assess the seismic vulnerability of buildings for the example city of Padang, Indonesia. Features are derived from remote sensing data to characterize the urban environment and are subsequently combined with in situ observations. Machine learning approaches are deployed in a sequential way to identify meaningful sets of features that are suitable to predict seismic vulnerability levels of buildings. When assessing the vulnerability level according to a scoring method, the overall mean absolute percentage erro

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Da, Yifan, Zhiyuan Ji, and Yongsheng Zhou. "Building Damage Assessment Based on Siamese Hierarchical Transformer Framework." Mathematics 10, no. 11 (2022): 1898. http://dx.doi.org/10.3390/math10111898.

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The rapid and accurate damage assessment of buildings plays a critical role in disaster response. Based on pairs of pre- and post-disaster remote sensing images, effective building damage level assessment can be conducted. However, most existing methods are based on Convolutional Neural Network, which has limited ability to learn the global context. An attention mechanism helps ameliorate this problem. Hierarchical Transformer has powerful potential in the remote sensing field with strong global modeling capability. In this paper, we propose a novel two-stage damage assessment framework called

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Xu, Hao, Panpan Zhu, Xiaobo Luo, Tianshou Xie, and Liqiang Zhang. "Extracting Buildings from Remote Sensing Images Using a Multitask Encoder-Decoder Network with Boundary Refinement." Remote Sensing 14, no. 3 (2022): 564. http://dx.doi.org/10.3390/rs14030564.

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Extracting buildings from high-resolution remote sensing images is essential for many geospatial applications, such as building change detection, urban planning, and disaster emergency assessment. Due to the diversity of geometric shapes and the blurring of boundaries among buildings, it is still a challenging task to accurately generate building footprints from the complex scenes of remote sensing images. The rapid development of convolutional neural networks is presenting both new opportunities and challenges with respect to the extraction of buildings from high-resolution remote sensing ima

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Yang, Wanting, Xianfeng Zhang, and Peng Luo. "Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake." Remote Sensing 13, no. 3 (2021): 504. http://dx.doi.org/10.3390/rs13030504.

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The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjust

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Miura, Hiroyuki, and Saburoh Midorikawa. "Updating GIS Building Inventory Data Using High-Resolution Satellite Images for Earthquake Damage Assessment: Application to Metro Manila, Philippines." Earthquake Spectra 22, no. 1 (2006): 151–68. http://dx.doi.org/10.1193/1.2162940.

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In order to conduct earthquake damage assessment, a methodology for updating GIS building inventory data in Metro Manila, Philippines, using remote sensing data is proposed. The locations of newly constructed mid- and high-rise buildings are detected from high-resolution satellite images using the image analysis technique, while the number of low-rise buildings is estimated from the built-up areas on a land cover classification map. The building inventory data is updated by incorporating the data on the newly constructed buildings into the existing data. The number of buildings in the updated

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Gullapalli, Venkata Lakshmi, R. RaghuNandanKumar, and G. R. Reddy. "Assessment of Antenna Mounting Building Structural Strength using Microtremor Analysis." IOP Conference Series: Materials Science and Engineering 1197, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1757-899x/1197/1/012057.

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Abstract Any satellite tracking ground station requires fully steerable dish shaped antenna for fixed satellite services. Antenna foundations require special consideration, because they transmit dynamic loads to soil and foundation system, in addition to static loads due to self-weight of foundation, antenna and its accessories. Antenna foundations are of ground foundation or roof top i.e on buildings based on Radio Frequency requirements. To install antennas on existing building, the building should have required strength. To assess the building strength, building natural frequency and dampin

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Zhang, Hongya, Chi Xu, Zhongjie Fan, Wenzhuo Li, Kaimin Sun, and Deren Li. "Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images." Applied Sciences 13, no. 19 (2023): 10729. http://dx.doi.org/10.3390/app131910729.

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Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation

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Liang, Chuan Zhi, and Meng Meng Lu. "Research on the Remote Monitoring System of Building Energy Consumption Based on GSM/GPRS." Applied Mechanics and Materials 197 (September 2012): 782–86. http://dx.doi.org/10.4028/www.scientific.net/amm.197.782.

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The paper aim the remote monitoring system of energy consumption in buildings based on GSM/GPRS by a case, and introduces the system framework, operating mode, transmission technology, equipments and installation requirements, as well as illustrating software composition and function of the data center. The system can reflect classified electricity consumption in monitored buildings correctly. It not only understand situation of energy consumption in real-time, but also offers basic data for further energy auditing and energy efficiency assessment. The conclusion can develop management of buil

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Cotrufo, Silvana, Constantin Sandu, Fabio Giulio Tonolo, and Piero Boccardo. "Building damage assessment scale tailored to remote sensing vertical imagery." European Journal of Remote Sensing 51, no. 1 (2018): 991–1005. http://dx.doi.org/10.1080/22797254.2018.1527662.

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Khandare, Shrinivas B., and Manoj B. Chandak. "Techniques of deep learning neural network-based building feature extraction from remote sensing images: a survey." International Journal of Informatics and Communication Technology (IJ-ICT) 14, no. 2 (2025): 614. https://doi.org/10.11591/ijict.v14i2.pp614-624.

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Recently, due to earthquake disaster, many people have lost their lives and homes, and not able to settle to new locations immediately. Therefore, a framework or a plan should be ready to immediately relocate the people to different locations or do resettlement. Much research has been done in this field but still there are problems of identifying clear building boundaries, rectangular houses, due to the problem of different shapes of the buildings. These techniques were explored for identification of clear building boundaries, rectangular houses, buildings which are more highlighted and smalle

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Kim, W., N. Kerle, and M. Gerke. "Mobile Augmented Reality in support of building damage and safety assessment." Natural Hazards and Earth System Sciences Discussions 3, no. 4 (2015): 2599–627. http://dx.doi.org/10.5194/nhessd-3-2599-2015.

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Abstract. Rapid and accurate assessment of the state of buildings in the aftermath of a disaster event is critical for an effective and timely response. For rapid damage assessment of buildings, the utility of remote sensing (RS) technology has been widely researched, with focus on a range of platforms and sensors. However, RS-based approach still have limitations to assess structural integrity and the specific damage status of individual buildings. Consequently, ground-based assessment conducted by structural engineers and first responders is still required. This paper demonstrates the concep

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Yue, W., and X. Zhao. "AN INVESTIGATION OF SUPER-RESOLUTION FOR CROSS-DOMAIN BUILDING EXTRACTION USING TRANSFORMER." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 153–58. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-153-2023.

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Abstract. The density of buildings is an important index to reflect the productivity and prosperity of an economic entity. Automatically monitoring the change and development of buildings through satellite can not only benefit the assessment of the status of urban development but also contribute to suburban construction planning. Apparently, more accurate building extraction performance can be guaranteed with higher-resolution remote sensing images. However, the desired high-resolution images are not always available limited by the remote sensing imaging technology and the expensive cost of up

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Kim, W., N. Kerle, and M. Gerke. "Mobile augmented reality in support of building damage and safety assessment." Natural Hazards and Earth System Sciences 16, no. 1 (2016): 287–98. http://dx.doi.org/10.5194/nhess-16-287-2016.

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Abstract. Rapid and accurate assessment of the state of buildings in the aftermath of a disaster event is critical for an effective and timely response. For rapid damage assessment of buildings, the utility of remote sensing (RS) technology has been widely researched, with focus on a range of platforms and sensors. However, RS-based approaches still have limitations to assess structural integrity and the specific damage status of individual buildings. Structural integrity refers to the ability of a building to hold the entire structure. Consequently, ground-based assessment conducted by struct

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18

Zhang, Rui, Heng Li, Kaifeng Duan, et al. "Automatic Detection of Earthquake-Damaged Buildings by Integrating UAV Oblique Photography and Infrared Thermal Imaging." Remote Sensing 12, no. 16 (2020): 2621. http://dx.doi.org/10.3390/rs12162621.

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Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) e

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Li, Cong, Zheng Chao Chen, Jia Jie Cui, and Meng Wang. "The Study of Building-Height Inversion Based on the Shadow of High-Resolution Satellite Images." Applied Mechanics and Materials 556-562 (May 2014): 5107–11. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5107.

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With the rapid development of the remote-sensing technology, more and more high-resolution remote-sensing data that currently available for rapid-assessment of the earthquake, disaster investigation and the extraction of building information are arised. Researching on the building-height inversion used the shadows of building has made some progress, but the formula was simple, the object of research was ideal as well. The paper gives the relation of building-height and shadow considered the impact of buildings, building-shadows, sensors, and the impact on the structure of shadow-image of relat

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Yang, Mingzhe, Yuan Zhou, Yanjie Feng, and Shuwei Huo. "Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images." Applied Sciences 14, no. 13 (2024): 5415. http://dx.doi.org/10.3390/app14135415.

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Building change detection monitors building changes by comparing and analyzing multi-temporal images acquired from the same area and plays an important role in land resource planning, smart city construction and natural disaster assessment. Different from change detection in conventional scenes, buildings in the building change detection task usually appear in a densely distributed state, which is easy to be occluded; at the same time, building change detection is easily interfered with by shadows generated by light and similar-colored features around the buildings, which makes the edges of th

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Pham, Thi-Thanh-Hiên, Philippe Apparicio, Christopher Gomez, Christiane Weber, and Dominique Mathon. "Towards a rapid automatic detection of building damage using remote sensing for disaster management." Disaster Prevention and Management 23, no. 1 (2014): 53–66. http://dx.doi.org/10.1108/dpm-12-2012-0148.

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Purpose – Satellite and airborne images are increasingly used at different stages of disaster management, especially in the detection of infrastructure damage. Although semi- or full automatic techniques to detect damage have been proposed, they have not been used in emergency situations. Damage maps produced by international organisations are still based on visual interpretation of images, which is time- and labour-consuming. The purpose of this paper is to investigate how an automatic mapping of damage can be helpful for a first and rapid assessment of building damage. Design/methodology/app

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Kaplan, Gordana, Resul Comert, Onur Kaplan, Dilek Kucuk Matci, and Ugur Avdan. "Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data." ISPRS International Journal of Geo-Information 11, no. 10 (2022): 517. http://dx.doi.org/10.3390/ijgi11100517.

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The extraction of building inventory information is vital for damage assessment and planning and modelling studies. In the last few years, the conventional data extraction for building inventory was overcome using various remote sensing data and techniques. The main objectives of this study were to supply the necessary data for the structural engineers to calculate the seismic performance of existing structures. Thus, we investigated light detection and ranging (LiDAR) derivatives data to classify buildings and extract building inventory information, such as different heights of the buildings

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Wang, Xianteng, Xue Li, Zhumei Liu, Zihao Wu, Yike Xie, and Zijie Han. "Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing." Sensors 25, no. 3 (2025): 744. https://doi.org/10.3390/s25030744.

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The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to

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Yu, Chen, Bin Hu, Xiuchuan Cheng, Guangqiang Yin, and Zhiguo Wang. "Remote sensing building damage assessment with a multihead neighbourhood attention transformer." International Journal of Remote Sensing 44, no. 16 (2023): 5069–100. http://dx.doi.org/10.1080/01431161.2023.2242590.

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Murao, Osamu, Takuma Usuda, Hideomi Gokon, et al. "Understanding Regional Building Characteristics in Yangon Based on Digital Building Model." Journal of Disaster Research 13, no. 1 (2018): 125–37. http://dx.doi.org/10.20965/jdr.2018.p0125.

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It is indispensable for a government to assess urban vulnerability to natural disasters such as earthquakes or flood in order to take appropriate disaster measures. However, it is sometimes difficult to obtain necessary dataset for cities or regions, especially for developing countries. The authors have been involved in a SATREPS project named “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar,” which aims to make urban vulnerability maps for Yangon City based on several datasets including building inventory of each ward. However, Yangon City has n

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Kaplan, Onur, and Gordana Kaplan. "Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake." Buildings 12, no. 1 (2021): 14. http://dx.doi.org/10.3390/buildings12010014.

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Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such

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Wen, Qi, Kaiyu Jiang, Wei Wang, et al. "Automatic Building Extraction from Google Earth Images under Complex Backgrounds Based on Deep Instance Segmentation Network." Sensors 19, no. 2 (2019): 333. http://dx.doi.org/10.3390/s19020333.

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Building damage accounts for a high percentage of post-natural disaster assessment. Extracting buildings from optical remote sensing images is of great significance for natural disaster reduction and assessment. Traditional methods mainly are semi-automatic methods which require human-computer interaction or rely on purely human interpretation. In this paper, inspired by the recently developed deep learning techniques, we propose an improved Mask Region Convolutional Neural Network (Mask R-CNN) method that can detect the rotated bounding boxes of buildings and segment them from very complex ba

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Wu, Jidong, Mengqi Ye, Xu Wang, and Elco Koks. "Building Asset Value Mapping in Support of Flood Risk Assessments: A Case Study of Shanghai, China." Sustainability 11, no. 4 (2019): 971. http://dx.doi.org/10.3390/su11040971.

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Exposure is an integral part of any natural disaster risk assessment, and damage to buildings is one of the most important consequence of flood disasters. As such, estimates of the building stock and the values at risk can assist in flood risk management, including determining the damage extent and severity. Unfortunately, little information about building asset value, and especially its spatial distributions, is readily available in most countries. This is certainly true in China, given that the statistical data on building floor area (BFA) is collected by administrative entities (i.e. census

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Becker, S., S. Einizinab, S. Radanovic, K. Khoshelham, K. Mirzaei, and Y. Fang. "REALITY CAPTURE METHODS FOR REMOTE INSPECTION OF BUILDING WORK." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 275–81. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-275-2023.

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Abstract. Conventional building inspection, which requires in-person visits by a qualified inspector, can be costly, time-consuming, and even pose health and safety risks. The travel restrictions of the global Covid-19 pandemic further highlighted the need for remote inspection methods. Using reality capture techniques to create a digital 3D representation of the site offers promise for remote inspection of building work. This paper aims to assess different reality capture methods and their visualizations for remote inspection of building work through a case study conducted in Melbourne, Austr

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Kumar, Kuldeep, and Virendra Kumar Paul. "Risk and Reliability Assessment of Smoke Control Systems in the Buildings." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 576–81. http://dx.doi.org/10.22214/ijraset.2022.47057.

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Abstract: During building fires, the most lethal factor affecting the occupants is the spread of smoke and toxic gases in the compartment, adjacent spaces, evacuation routes, and locations that are remote from the fire origin threatening life and damage to the property of the facility. Smoke and heat control in building fires is a major challenge for the egress of occupants as well as the fire-fighting operations. This paper provides an insight into risks due to the failure of smoke control and ventilation systems. Life and fire safety in and outside the building must also be analyzed in order

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Ou, Shengya, Mingquan Wu, Zheng Niu, et al. "Remote Sensing Identification and Analysis of Global Building Electrification (2012–2023)." Remote Sensing 17, no. 5 (2025): 777. https://doi.org/10.3390/rs17050777.

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The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in terms of temporal discontinuity and restricted threshold selection. To effectively address these issues, in this work, an improved remote sensing method was proposed to monitor global building electrification. By integrating global land cover data, built-up area data, and annual NPP/

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Wang, Yu, Liangyi Cui, Chenzong Zhang, Wenli Chen, Yang Xu, and Qiangqiang Zhang. "A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images." Remote Sensing 14, no. 4 (2022): 1012. http://dx.doi.org/10.3390/rs14041012.

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Large-scale optical sensing and precise, rapid assessment of seismic building damage in urban communities are increasingly demanded in disaster prevention and reduction. The common method is to train a convolutional neural network (CNN) in a pixel-level semantic segmentation approach and does not fully consider the characteristics of the assessment objectives. This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, s

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Zareena Begum, Chittimalla Rohith Kumar, R. Dinesh, Muppidi Vishal, and Prashant Bidave. "ADAPTIVE TRAINING SAMPLE SELECTION FOR REMOTE SENSING-BASED BUILDING DAMAGE ASSESSMENT IN DISASTER SCENARIOS." Scientific Digest : Journal of Applied Engineering 12, no. 1 (2025): 486–91. https://doi.org/10.70864/joae.2024.v12.i1.pp486-491.

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Accurate classification of building damage caused by disasters such as earthquakes, hurricanes, and floods is crucial for effective disaster response and recovery. Remote sensing technology provides timely and extensive spatial data, making it a valuable tool for large-scale damage assessment. However, conventional training sample selection methods, which often rely on random or evenly distributed sampling, can be inefficient and fail to capture the full spectrum of damage severities. These traditional approaches may lead to suboptimal classification performance and require extensive manual ef

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Abdessetar, M., and Y. Zhong. "BUILDINGS CHANGE DETECTION BASED ON SHAPE MATCHING FOR MULTI-RESOLUTION REMOTE SENSING IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 683–87. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-683-2017.

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Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of

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Kabzhanova, Gulnara, Ranida Arystanova, Anuarbek Bissembayev, et al. "Remote Sensing Applications for Pasture Assessment in Kazakhstan." Agronomy 15, no. 3 (2025): 526. https://doi.org/10.3390/agronomy15030526.

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Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potentia

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Liu, Jun, Yigang Luo, Sha Chen, Jidong Wu, and Ying Wang. "BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data." Applied Sciences 14, no. 10 (2024): 3964. http://dx.doi.org/10.3390/app14103964.

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Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unm

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Mharzi Alaoui, H., H. Radoine, J. Chenal, H. Hajji, and H. Yakubu. "DEEP BUILDING FOOTPRINT EXTRACTION FOR URBAN RISK ASSESSMENT – REMOTE SENSING AND DEEP LEARNING BASED APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 83–86. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-83-2022.

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Abstract. Mapping building footprints can play a crucial role in urban dynamics moni-toring, risk assessment and disaster management. Available free building footprints, like OpenStreetMap, provide manually annotated building foot-print information for some urban areas; however, frequently it does not en-tirely cover urban areas in many parts of the world and is not always availa-ble. The huge potential for meaningful ground information extraction from high-resolution Remote Sensing imagery can be considered as an alternative and a reliable source of data for building footprint generation. The

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Ahmadi, Seyed Ali, Ali Mohammadzadeh, Naoto Yokoya, and Arsalan Ghorbanian. "BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images." Remote Sensing 16, no. 1 (2023): 182. http://dx.doi.org/10.3390/rs16010182.

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When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two bran

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Hancilar, Ufuk, Fabio Taucer, and Christina Corbane. "Empirical Fragility Functions based on Remote Sensing and Field Data after the 12 January 2010 Haiti Earthquake." Earthquake Spectra 29, no. 4 (2013): 1275–310. http://dx.doi.org/10.1193/121711eqs308m.

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In the immediate aftermath of the Haiti earthquake of 12 January 2010, a joint study for the estimation of damage to the building stock based on aerial images was carried out by UNITAR-UNOSAT, the EC-JRC, and the World Bank/ImageCAT in support of the PDNA. A targeted field campaign was led to the areas affected by the disaster in collaboration with the CNIGS with the purpose of validating the remote sensing based damage assessment. These two methodologies for collecting data resulted in two data sets of the damaged buildings categorized according to the EMS-98 damage grades. In the present stu

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Foulser-Piggott, Roxane, Robin Spence, Ron Eguchi, and Andrew King. "Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake." Earthquake Spectra 32, no. 1 (2016): 611–31. http://dx.doi.org/10.1193/051214eqs067m.

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This study explores the performance of GEOCAN, a remote-sensing and crowdsourcing platform for assessing earthquake damage, by using geo-referenced ground-based damage assessments. This paper discusses methods for the application of remote sensing in post-earthquake damage assessment and reports on a GEOCAN crowd-sourcing study following the 22 February 2011 Christchurch event and its validation using field studies. It describes the principal data sets used, discusses in detail the problems of validation, and considers the extent of omission and commission errors. It is clear that although com

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Xie, Shizhe, Dongping Ming, Jin Yan, Huaining Yang, Ran Liu, and Zhi Zhao. "Research on Fine Estimation of People Trapped after Earthquake on Single Building Level Based on Multi-Source Data." Applied Sciences 13, no. 9 (2023): 5430. http://dx.doi.org/10.3390/app13095430.

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Risk assessments of people who are trapped are an important basis for scientific and effective emergency rescue after an earthquake. Currently, most models are based on the kilometer grid scale or community scale that gauge the population and extent of the earthquake burial under distinct intensities. The estimation results of the methods are on coarse scales; therefore, the methods cannot meet the requirements of rapid rescue after an earthquake. In response to the above statements, this study uses multi-source data to propose a way to estimate the number and distribution of people trapped un

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Rui, Xue, Yang Cao, Xin Yuan, Yu Kang, and Weiguo Song. "DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation." Remote Sensing 13, no. 21 (2021): 4284. http://dx.doi.org/10.3390/rs13214284.

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Rapid progress on disaster detection and assessment has been achieved with the development of deep-learning techniques and the wide applications of remote sensing images. However, it is still a great challenge to train an accurate and robust disaster detection network due to the class imbalance of existing data sets and the lack of training data. This paper aims at synthesizing disaster remote sensing images with multiple disaster types and different building damage with generative adversarial networks (GANs), making up for the shortcomings of the existing data sets. However, existing models a

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Lin, Qigen, Tianyu Ci, Leibin Wang, Sanjit Kumar Mondal, Huaxiang Yin, and Ying Wang. "Transfer Learning for Improving Seismic Building Damage Assessment." Remote Sensing 14, no. 1 (2022): 201. http://dx.doi.org/10.3390/rs14010201.

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The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly ear

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YAMAZAKI, FUMIO, and MASASHI MATSUOKA. "REMOTE SENSING TECHNOLOGIES IN POST-DISASTER DAMAGE ASSESSMENT." Journal of Earthquake and Tsunami 01, no. 03 (2007): 193–210. http://dx.doi.org/10.1142/s1793431107000122.

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This paper highlights the recent applications of remote sensing technologies in post-disaster damage assessment, especially in the 2004 Indian Ocean tsunami and the 2006 Central Java earthquake. After the 2004 Indian Ocean tsunami, satellite images which captured the affected areas before and after the event were fully employed in field investigations and in tsunami damage mapping. Since the affected areas are vast, moderate resolution satellite images were quite effective in change detection due to the tsunami. Using high-resolution optical satellite images acquired before and after the 2006

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Chen, Jin, Hong Tang, Jiayi Ge, and Yaozhong Pan. "Rapid Assessment of Building Damage Using Multi-Source Data: A Case Study of April 2015 Nepal Earthquake." Remote Sensing 14, no. 6 (2022): 1358. http://dx.doi.org/10.3390/rs14061358.

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It is of great significance for emergency rescue to rapidly assess damage of buildings after an earthquake. Some previous methods are time-consuming, data are difficult to obtain, or there is lack of regional damage assessment. We proposed a novel way to rapidly assess building damage by comprehensively utilizing earth observation-derived data and field investigation to alleviate the above problems. These data are related to hazard-causing factors, hazard-formative environment, and hazard-affected body. Specifically, predicted ground motion parameters are used to reflect hazard-causing factors

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Kholoshyn, I. V., M. J. Syvyj, S. V. Mantulenko, O. L. Shevchenko, D. Sherick, and K. M. Mantulenko. "Assessment of military destruction in Ukraine and its consequences using remote sensing." IOP Conference Series: Earth and Environmental Science 1254, no. 1 (2023): 012132. http://dx.doi.org/10.1088/1755-1315/1254/1/012132.

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Abstract The article raises the problem of using Earth remote sensing data to collect evidence of damages caused by the military actions of the Russian army in Ukraine. The core data set obtained by deciphering aerial photographs reflects the general current and operational situation in the affected area, reducing the subjectivity and uncertainty of damage characteristics on the ground. Earth remote sensing data visualize visible damage to the environment, which can be recognized and assessed using images of different spectral bands with appropriate resolution. Among the damage caused to the e

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Lemoine, G., C. Corbane, C. Louvrier, and M. Kauffmann. "Intercomparison and validation of building damage assessments based on post-Haiti 2010 earthquake imagery using multi-source reference data." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (2013): 1445–86. http://dx.doi.org/10.5194/nhessd-1-1445-2013.

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Abstract. The Haiti 2010 earthquake is one of the first major disasters in which very high resolution satellite and airborne imagery was embraced to delineate the event impact. Several rapid mapping initiatives exploited post-earthquake satellite and airborne imagery to produce independent point feature sets marking the damage grade of affected buildings. Despite the obvious potential of the satellite remote sensing technology in providing damage figures, the scale and complexity of the urban structures in Port-au-Prince cause overall figures and patterns of the damage assessments to yield a r

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Xie, Zhengtao, Zifan Zhou, Xinhao He, Yuguang Fu, Jiancheng Gu, and Jiandong Zhang. "Methodology for Object-Level Change Detection in Post-Earthquake Building Damage Assessment Based on Remote Sensing Images: OCD-BDA." Remote Sensing 16, no. 22 (2024): 4263. http://dx.doi.org/10.3390/rs16224263.

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Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accurac

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Hattula, Emilia, Lingli Zhu, and Jere Raninen. "Building extraction in urban and rural areas with aerial and LiDAR DSM." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W4-2024 (May 31, 2024): 73–79. http://dx.doi.org/10.5194/isprs-annals-x-4-w4-2024-73-2024.

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Abstract. Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study

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Römer, H., P. Willroth, G. Kaiser, et al. "Potential of remote sensing techniques for tsunami hazard and vulnerability analysis – a case study from Phang-Nga province, Thailand." Natural Hazards and Earth System Sciences 12, no. 6 (2012): 2103–26. http://dx.doi.org/10.5194/nhess-12-2103-2012.

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Abstract. Recent tsunami disasters, such as the 2004 Indian Ocean tsunami or the 2011 Japan earthquake and tsunami, have highlighted the need for effective risk management. Remote sensing is a relatively new method for risk analysis, which shows significant potential in conducting spatially explicit risk and vulnerability assessments. In order to explore and discuss the potential and limitations of remote sensing techniques, this paper presents a case study from the tsunami-affected Andaman Sea coast of Thailand. It focuses on a local assessment of tsunami hazard and vulnerability, including t

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