ACM Journal on Computing and Sustainable Societies最新文献

筛选
英文 中文
Pixel Perfect: Using Vision Transformers to Improve Road Quality Predictions from Medium Resolution and Heterogeneous Satellite Imagery 像素完美:使用视觉变压器改善中分辨率和异构卫星图像的道路质量预测
ACM Journal on Computing and Sustainable Societies Pub Date : 2023-07-31 DOI: 10.1145/3608112
Aggrey Muhebwa, Gabriel Cadamuro, Jay Taneja
{"title":"Pixel Perfect: Using Vision Transformers to Improve Road Quality Predictions from Medium Resolution and Heterogeneous Satellite Imagery","authors":"Aggrey Muhebwa, Gabriel Cadamuro, Jay Taneja","doi":"10.1145/3608112","DOIUrl":"https://doi.org/10.1145/3608112","url":null,"abstract":"Critical infrastructure, such as roads and electricity, are core systems that enable economic development. However, these crucial systems are frequently under-monitored in developing regions, resulting in lost opportunities for growth. Recent advances in remote sensing and machine learning have enabled monitoring and measurement of infrastructure faster and more frequently than traditional methods. However, ground data are often unavailable, resulting in a disconnect between labels and remotely sensed data. Furthermore, data from industrialized regions can only sometimes be transferred to regions with sparse data due to differences in the concept of quality between regions. Additionally, inconsistency in data and the complexity of ML models can introduce bias due to learned characteristics across diverse regions, leading to inaccurate predictions and recommendations for action. In this study, we train and compare traditional neural networks and vision transformers to predict road quality from medium-resolution satellite imagery and apply them to realistic data conditions: heterogeneous temporal-spatial resolutions. The best models (vision transformers) achieve AUROC scores of 0.934 and 0.685 for binary and five-class classification tasks, respectively, exhibiting results appealing for inference in otherwise unmeasured areas. Furthermore, these experiments and results showed that proper training techniques could produce accurate models from limited, heterogeneous, and low-resolution data.","PeriodicalId":238057,"journal":{"name":"ACM Journal on Computing and Sustainable Societies","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123458277","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信