Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications最新文献

A study on Singapore's vegetation cover and land use change using remote sensing 新加坡植被覆盖与土地利用变化的遥感研究

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications Pub Date : 2022-11-01 DOI: 10.1145/3557922.3567480

Yun Si Goh, Jing Wen Leong, Seanglidet Yean, B. Lee, K. M. Ngo, Pete Edwards

引用次数: 0

BinoML

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications Pub Date : 2022-11-01 DOI: 10.1145/3557922.3567481

Puyang Ma, Ravi Garg, Mohamed M. A. Moustafa

{"title":"BinoML","authors":"Puyang Ma, Ravi Garg, Mohamed M. A. Moustafa","doi":"10.1145/3557922.3567481","DOIUrl":"https://doi.org/10.1145/3557922.3567481","url":null,"abstract":"Building numbers shown on building outlines of a map are important information for guiding delivery associates to the correct building of a package's recipient. Intuitively, the more labeled buildings are present in our map, the less likely to misplace an order in addition to other benefits such as delivery efficiency as drivers get better visual cues about building positions. Although there are free and collaborative projects for creating geographic database of the world, such as the OpenStreetMap (OSM) [2] which also supplies building outlines along with their building numbers, many building outlines still remain unlabeled in many U.S. regions and other countries. Hence, we are interested in developing models that can automatically add building numbers with ≥ 99% precision to unlabeled buildings across geographies with low to medium building number coverage. In this paper, we describe a ML model which in offline results showed 2% to 12% increase in building number coverage in some US regions compared to that of the OSM. The proposed model can also be applied to improve the building number coverage of other countries after fine-tuning to those new regions.","PeriodicalId":393750,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117344914","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

Toward a crowdsourcing solution to estimate border crossing times using market-available connected vehicle data 采用众包解决方案，利用市场上可获得的联网车辆数据来估计过境时间

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications Pub Date : 2022-11-01 DOI: 10.1145/3557922.3567669

Ehsan Jalilifar, Xiao Li, Michael E. Martin, Xiao-Xiao Huang

{"title":"Toward a crowdsourcing solution to estimate border crossing times using market-available connected vehicle data","authors":"Ehsan Jalilifar, Xiao Li, Michael E. Martin, Xiao-Xiao Huang","doi":"10.1145/3557922.3567669","DOIUrl":"https://doi.org/10.1145/3557922.3567669","url":null,"abstract":"Effectively monitoring border crossing time is of great importance to various stakeholders. Border crossing information systems currently implemented along the United States-Mexico border require a large installed base of sensors, costly for installation and maintenance. This study provides a preliminary assessment of market-available connected vehicle (CV) data in border crossing time estimation. We evaluated one week of CV data collected at the Paso del Norte (PDN) port of entry (POE). We used a set of big data analytic tools to process big CV datasets and generated CV-based border crossing times (CV-Time). Then, we evaluated the correlation between the CV-Time and the existing Bluetooth-generated border crossing times (Bluetooth-Time) at the PDN POE. Last, we built a regression model to estimate the Bluetooth-Time (ground truth data) based on CV-based variables. The results demonstrate that the CV-Time is strongly correlated with the Bluetooth-Time, with a correlation rate of approximately 0.89. This study demonstrates that the market-available CV data is a potential data source for monitoring border crossing times.","PeriodicalId":393750,"journal":{"name":"Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116239618","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

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications 第一届ACM sigspace空间大数据与人工智能工业应用国际研讨会论文集

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications Pub Date : 1900-01-01 DOI: 10.1145/3557922

引用次数: 0