Sonu Kumar, Edward Park, Dung Duc Tran, Jingyu Wang, Huu Loc Ho, Lian Feng, Sameh A. Kantoush, Doan Van Binh, Dongfeng Li, Adam D. Switzer
{"title":"绘制大型热带三角洲河床采砂预算的深度学习框架","authors":"Sonu Kumar, Edward Park, Dung Duc Tran, Jingyu Wang, Huu Loc Ho, Lian Feng, Sameh A. Kantoush, Doan Van Binh, Dongfeng Li, Adam D. Switzer","doi":"10.1080/15481603.2023.2285178","DOIUrl":null,"url":null,"abstract":"Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our under...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"98 1","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep learning framework to map riverbed sand mining budgets in large tropical deltas\",\"authors\":\"Sonu Kumar, Edward Park, Dung Duc Tran, Jingyu Wang, Huu Loc Ho, Lian Feng, Sameh A. Kantoush, Doan Van Binh, Dongfeng Li, Adam D. Switzer\",\"doi\":\"10.1080/15481603.2023.2285178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our under...\",\"PeriodicalId\":55091,\"journal\":{\"name\":\"GIScience & Remote Sensing\",\"volume\":\"98 1\",\"pages\":\"\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2023-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GIScience & Remote Sensing\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/15481603.2023.2285178\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GIScience & Remote Sensing","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/15481603.2023.2285178","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
A deep learning framework to map riverbed sand mining budgets in large tropical deltas
Rapid urbanization has dramatically increased the demand for river sand, leading to soaring sand extraction rates that often exceed natural replenishment in many rivers globally. However, our under...
期刊介绍：
GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
