Ariane Droin

Lehrstuhl für Fernerkundung
+49 (0)931 31-88607
ariane.droin@uni-wuerzburg.de
Institut für Geographie und Geologie
Lehrstuhl für Fernerkundung
John Skilton Str. 4a
97074 Würzburg
nach Vereinbarung
2024[ to top ]
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How does pedestrian permeability vary in and across cities? A fine-grained assessment for all large cities in Germany. In: Computers, Environment and Urban Systems, 110, 102115.. (2024):
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Liveability in large housing estates in Germany – Identifying differences based on a novel concept for a walkable city. In: Landscape and Urban Planning, 251. (2024):
2023[ to top ]
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In the tension between large-scale analysis and accuracy - Identifying and analysing intra-urban (sub-)centre structures comparing official 3D-building models and TanDEM-X nDSMs. In: Computers, Environment and Urban Systems, 102, 101953.. (2023):
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Quantifying urban heat exposure at fine scale - modeling outdoor and indoor temperatures using citizen science and VHR remote sensing. In: Urban Climate, 49, 101522.. (2023):
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Are public green spaces distributed fairly? A nationwide analysis based on remote sensing, OpenStreetMap and census data. In: Geocarto International, 38 (1). (2023):
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Greener cities cost more green: Examining the impacts of different urban expansion patterns on NPP. In: Building and Environment, 228. (2023):
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The Individual Walkable Neighborhood - Evaluating people-centered spatial units focusing on urban density. In: Computers, Environment and Urban Systems, 99. (2023):
2022[ to top ]
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Morphometrische Ableitung von Fließfacetten anhand von hochauflösenden Structure from Motion (SfM) Geländemodellen. In: Die Höhle, 73 (1-4). (2022):
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To be, or not to be ‘urban’? A multi-modal method for the differentiated measurement of the degree of urbanization. In: Computers, Environment and Urban Systems, 95, 101830.. (2022):
2021[ to top ]
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Spatial factors influencing building age prediction and implications for urban residential energy modelling. In: Computers, Environment and Urban Systems, 88. (2021):
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Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling. In: ISPRS Journal of Photogrammetry and Remote Sensing, 10 (23), 1-20.. (2021):
2021[ to top ]
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Collecting data for urban building energy modelling by remote sensing and machine learning. In: Proceedings of Building Simulation 2021: 17th Conference of IBPSABuilding Simulation, 1139-46.. (2021):