Int. J. Appl. Earth Obs. Geoinformation最新文献

Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data 利用虚拟激光扫描数据训练的机器学习模型对多时摄影测量点云的结构建筑损伤等级进行分类

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2023-02-24 DOI: 10.48550/arXiv.2302.12591

V. Zahs, K. Anders, Julia Kohns, Alexander Stark, B. Höfle

{"title":"Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data","authors":"V. Zahs, K. Anders, Julia Kohns, Alexander Stark, B. Höfle","doi":"10.48550/arXiv.2302.12591","DOIUrl":"https://doi.org/10.48550/arXiv.2302.12591","url":null,"abstract":"Automatic damage assessment based on UAV-derived 3D point clouds can provide fast information on the damage situation after an earthquake. However, the assessment of multiple damage grades is challenging due to the variety in damage patterns and limited transferability of existing methods to other geographic regions or data sources. We present a novel approach to automatically assess multi-class building damage from real-world multi-temporal point clouds using a machine learning model trained on virtual laser scanning (VLS) data. We (1) identify object-specific change features, (2) separate changed and unchanged building parts, (3) train a random forest machine learning model with VLS data based on object-specific change features, and (4) use the classifier to assess building damage in real-world point clouds from photogrammetry-based dense image matching (DIM). We evaluate classifiers trained on different input data with respect to their capacity to classify three damage grades (heavy, extreme, destruction) in pre- and post-event DIM point clouds of a real earthquake event. Our approach is transferable with respect to multi-source input point clouds used for training (VLS) and application (DIM) of the model. We further achieve geographic transferability of the model by training it on simulated data of geometric change which characterises relevant damage grades across different geographic regions. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%). Its performance improves only slightly when using real-world region-specific training data (<3% higher overall accuracies) and when using real-world region-specific training data (<2% higher overall accuracies). We consider our approach relevant for applications where timely information on the damage situation is required and sufficient real-world training data is not available.","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79015399","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: 2

Example-Based Explainable AI and its Application for Remote Sensing Image Classification 基于实例的可解释人工智能及其在遥感图像分类中的应用

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2023-02-03 DOI: 10.48550/arXiv.2302.01526

Shin-nosuke Ishikawa, Masato Todo, M. Taki, Y. Uchiyama, Kazunari Matsunaga, Pei-Ru Lin, Taiki Ogihara, Masao Yasui

{"title":"Example-Based Explainable AI and its Application for Remote Sensing Image Classification","authors":"Shin-nosuke Ishikawa, Masato Todo, M. Taki, Y. Uchiyama, Kazunari Matsunaga, Pei-Ru Lin, Taiki Ogihara, Masao Yasui","doi":"10.48550/arXiv.2302.01526","DOIUrl":"https://doi.org/10.48550/arXiv.2302.01526","url":null,"abstract":"We present a method of explainable artificial intelligence (XAI),\"What I Know (WIK)\", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression.","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76655502","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: 4

Mapping the net ecosystem exchange of CO2 of global terrestrial systems 绘制全球陆地系统二氧化碳净生态系统交换图

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2023-02-01 DOI: 10.2139/ssrn.4058420

Yi Lian, Haixiao Li, Qianqian Renyang, Le Liu, Jiankang Dong, Xiaoning Liu, Zihan Qu, Lien-Chieh Lee, Lei Chen, Dongliang Wang, Hu Zhang

引用次数: 1

Fusing GEDI with earth observation data for large area aboveground biomass mapping 融合GEDI与地球观测数据进行大面积地上生物量制图

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103108

Yuri Shendryk

引用次数: 11

Investigating deformation along metro lines in coastal cities considering different structures with InSAR and SBM analyses 利用InSAR和SBM分析方法研究不同结构的沿海城市地铁沿线变形

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103099

Ru Wang, Mengshi Yang, Jiefang Dong, M. Liao

引用次数: 5

A multi-layer fusion image enhancement method for visual odometry under poor visibility scenarios 一种用于低能见度场景下视觉里程计的多层融合图像增强方法

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103118

Y. Qi, Chongxing Liu, Hangbin Wu, Xiaohang Shao, Junyi Wei

引用次数: 0

Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map 利用全球生物量图估算秘鲁亚马逊地区次国家级森林AGB的精度

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103102

N. Málaga, S. Bruin, R. McRoberts, Alexs Arana Olivos, R. Paiva, Patricia Durán Montesinos, Daniela Requena Suarez, M. Herold

引用次数: 5

Extreme rainfall-related accelerations in landslides in Danba County, Sichuan Province, as detected by InSAR InSAR探测到的四川省丹巴县与极端降雨有关的滑坡加速

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103109

Xuguo Shi, Jianing Wang, M. Jiang, Shaocheng Zhang, Yunlong Wu, Yulong Zhong

引用次数: 4

Forest foliage fuel load estimation from multi-sensor spatiotemporal features 基于多传感器时空特征的森林叶片燃料负荷估算

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103101

Yanxi Li, R. Chen, Bin He, S. Veraverbeke

引用次数: 3

Inventory of close-to-nature forest stands using terrestrial mobile laser scanning 利用地面移动激光扫描对接近自然的林分进行清查

Int. J. Appl. Earth Obs. Geoinformation Pub Date : 2022-12-01 DOI: 10.1016/j.jag.2022.103104

Karel Kuželka, R. Marušák, P. Surový

引用次数: 1