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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. (2024): 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.
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. (2024): Liveability in large housing estates in Germany – Identifying differences based on a novel concept for a walkable city. In: Landscape and Urban Planning, 251
2023[ to top ]
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. (2023): 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.
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. (2023): Quantifying urban heat exposure at fine scale - modeling outdoor and indoor temperatures using citizen science and VHR remote sensing. In: Urban Climate, 49, 101522.
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. (2023): Are public green spaces distributed fairly? A nationwide analysis based on remote sensing, OpenStreetMap and census data. In: Geocarto International, 38 (1)
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. (2023): Greener cities cost more green: Examining the impacts of different urban expansion patterns on NPP. In: Building and Environment, 228
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. (2023): The Individual Walkable Neighborhood - Evaluating people-centered spatial units focusing on urban density. In: Computers, Environment and Urban Systems, 99
2022[ to top ]
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. (2022): Morphometrische Ableitung von Fließfacetten anhand von hochauflösenden Structure from Motion (SfM) Geländemodellen. In: Die Höhle, 73 (1-4)
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. (2022): 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.
2021[ to top ]
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. (2021): Spatial factors influencing building age prediction and implications for urban residential energy modelling. In: Computers, Environment and Urban Systems, 88
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. (2021): 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[ to top ]
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. (2021): 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.
