[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2440":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":24,"paper":25,"created_at":47},2440,"Mapping urban expansion and land transformation in Dhaka, Bangladesh by fusing night-time lights, thermal, and spectral data via machine learning approaches","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cacint.2026.100472","This study evaluates urban expansion and land transformation in Dhaka District, Bangladesh, between 2016 and 2024 by integrating multi-source geospatial data with machine learning. The aim was to map urban growth more precisely than conventional spectral indices allow and to characterize its spatial patterns through landscape metrics and change detection. Landsat 8 thermal and spectral data were fused with Visible Infrared Imaging Radiometer Suite (VIIRS) night-time lights, a suite of spectral indices (NDVI, NDBI, SAVI, MNDWI), Gray-Level Co-occurrence Matrix (GLCM) texture features, and road-network data. Three supervised classifiers, Random Forest (RF), Support Vector Machine (SVM), and Gradient Tree Boosting (GTB), together with a majority-voting ensemble, were compared. Random Forest performed best in every study year (overall accuracy 99.57–99.62%; Kappa 0.991–0.992) and was adopted as the urban layer for all subsequent analyses. The urban area expanded by 39.53 km 2 over the period. Vegetation and agricultural land were the predominant source of new urban land (75.6%), whereas direct water-to-urban conversion was minor (below 2%); total water-body area nonetheless declined by roughly 25% (from 79.0 to 58.9 km 2 ), largely through indirect conversion to vegetation and seasonal land rather than through direct urbanization. Landscape metrics indicate increasingly fragmented growth, with patch density rising from 2.46 to 7.82 per 100 ha and Shannon's entropy from 0.45 to 0.49. Zonal analysis identifies the northeastern and southeastern peri -urban fringes as the fastest-growing, dominated by leapfrog and edge development. The findings depict an unsustainable expansion trajectory whose environmental costs stem from the direct loss of vegetation and agricultural land alongside indirect pressure on floodplain water bodies, and they offer a transferable multi-modal framework for monitoring sprawl in Global South megacities. The application of the machine learning and geospatial approach in this study will help urban planners, policymakers, and stakeholders manage Dhaka's rapid and fragmented urban growth.","本研究通过整合多源地理空间数据与机器学习方法，评估了2016年至2024年间孟加拉国达卡地区的城市扩张与土地转型。研究旨在比传统光谱指数更精确地绘制城市增长图景，并通过景观格局指标与变化检测刻画其空间模式。研究将Landsat 8热红外与光谱数据与可见光红外成像辐射仪套件（VIIRS）夜间灯光数据、一组光谱指数（NDVI、NDBI、SAVI、MNDWI）、灰度共生矩阵（GLCM）纹理特征以及道路网络数据进行融合。比较了三种监督分类器——随机森林（RF）、支持向量机（SVM）和梯度树提升（GTB），以及多数投票集成方法。随机森林在各研究年份均表现最佳（总体精度99.57%–99.62%；Kappa系数0.991–0.992），并被采纳为后续所有分析的城市图层。研究期内城市面积扩张了39.53 km²。植被和农业用地是新增城市用地的主要来源（75.6%），而直接的水体向城市用地转化较少（低于2%）；然而，水体总面积仍下降了约25%（从79.0 km²降至58.9 km²），这主要是通过间接转化为植被和季节性土地而非直接城市化实现的。景观格局指标表明增长日益破碎化，斑块密度从每100 ha 2.46上升至7.82，香农熵从0.45上升至0.49。分区分析识别出东北部和东南部城市边缘区为增长最快的区域，以跳跃式和边缘式开发为主。研究结果描绘了一条不可持续的扩张轨迹，其环境代价既来自植被和农业用地的直接丧失，也来自对洪泛平原水体的间接压力，并为监测全球南方特大城市蔓延提供了一个可迁移的多模态框架。本研究中的机器学习与地理空间方法的应用将有助于城市规划者、政策制定者和利益相关者管理达卡快速且破碎化的城市增长。",null,"City and Environment Interactions","2026-09-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究孟加拉国达卡城市扩张与土地转化，属城市遥感与城市规划领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"机器学习","遥感监测","城市扩张","10.1016\u002Fj.cacint.2026.100472",{"doi":24,"openalex_id":26,"authors":27,"venue":10,"cited_by_count":15,"oa_url":6,"card":39,"direction":45,"ingested_from":46},"W7212257463",[28,30,33,35,37],{"name":29,"orcid":9},"Arpon Sarkar",{"name":31,"orcid":32},"Mafrid Haydar","https:\u002F\u002Forcid.org\u002F0009-0003-9229-0171",{"name":34,"orcid":9},"Farzana Islam Mitu",{"name":36,"orcid":9},"Al Hossain Rafi",{"name":38,"orcid":9},"Sakib Hosan",{"tldr":40,"method":41,"finding":42,"direction":43,"opportunity":44},"融合夜间灯光、热红外与光谱数据，用机器学习精准监测达卡2016-2024年城市扩张与土地转化。","Landsat 8热红外与光谱、VIIRS夜间灯光、NDVI\u002FNDBI等指数、G","城市扩张39.53 km²，75.6%来自植被和农地，水体间接减少约25%，增长日益破碎化。","农业遥感与作物表型","可将该多模态融合框架迁移至耕地流失预警与城郊农地保护，结合时序模型预测扩张对农业的长期影响。","农业人工智能与决策模型","openalex","2026-09-14T23:30:47.470495Z"]