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Robert Hecht

Leibniz Institute of Ecological and Regional Development (IOER)

SCHOLARLY PAPERS

2

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Scholarly Papers (2)

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1.

Approach to an Unknown: Representative Sample Survey to Explore Structure and Energy-Related Quality of the Non-Residential Building Stock in Germany

Number of pages: 29 Posted: 03 May 2023
Michael Hoerner, Holger Cischinsky, Martin Behnisch, Roland Busch, Julian Bischof, Markus Rodenfels, André Hartmann, Robert Hecht, Gotthard Meinel, Martin Schorcht, Steffen Schwarz, Guido Spars and Ann-Kathrin Tigges
Institute for Housing and Environment (Institut Wohnen und Umwelt (IWU)), Institute for Housing and Environment (Institut Wohnen und Umwelt (IWU)), Leibniz Institute of Ecological and Regional Development (IOER) - Research Area Spatial Information and Modelling, University of Wuppertal, Institute for Housing and Environment (Institut Wohnen und Umwelt (IWU)), R+W Antriebselemente, Leibniz Institute of Ecological and Regional Development (IOER), Leibniz Institute of Ecological and Regional Development (IOER), Leibniz Institute of Ecological and Regional Development (IOER), Leibniz Institute of Ecological and Regional Development (IOER), Leibniz Institute of Ecological and Regional Development (IOER), University of Wuppertal and University of Wuppertal
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Non-residential building, building stock, energy renovation rate, representative sample survey, Horvitz-Thompson estimator, geospatial data

2.

Mapping of Bee-Friendly Trees Through Remote Sensing: A Novel Approach to Enhance Pollinator Conservation

Number of pages: 27 Posted: 10 Dec 2024
Robbe Neyns, Markus Münzinger, Robert Hecht, Henri Greil, Frank Canters and Hanna Gardein
affiliation not provided to SSRN, Leibniz Institute of Ecological and Regional Development (IOER) - Research Area Spatial Information and Modelling, Leibniz Institute of Ecological and Regional Development (IOER), affiliation not provided to SSRN, Vrije Universiteit Brussel (VUB) and affiliation not provided to SSRN
Downloads 68 (907,769)

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Solitary bees, Foraging distance, PlanetScope, LiDAR, Deep Learning, Tree mapping