This repository contains the scripts developed for the following research paper:
Weigand, M., Wurm, M., Droin, A., Stark, T., Staab, J., Rauh, J., Taubenböck, H. (2023). Are public green spaces distributed fairly? A nationwide analysis based on remote sensing, OpenStreetMap and census data. Geocarto International 38(1), 2286305. https://doi.org/10.1080/10106049.2023.2286305
The code was developed at the German Aerospace Center (DLR)
01_green_dbcontains all scripts required to set up and fill a PostgreSQL/PostGIS database that is used for efficient data access. Scripts are ordered numerically.02_green_extractcontains functionality to extract green space related information as part of preprocessing. A.h5array is created to facilitate efficient ingestion into the deep learning pipeline. The entry point ish5extractor.py.03_fusion_greencontains the TensorFlow-based deep learning pipeline for training, evaluating and predicting public green space availability on neighborhood scale nationwide. The central entry point isexperiment.pyand the configuration used in the published paper is located inruns/final_run/config.yml.
This software is licensed under the Apache 2.0 License.
Copyright (c) 2023 German Aerospace Center (DLR) * German Remote Sensing Data Center * Department: Geo-Risks and Civil Security
The UKIS team welcomes contributions from the community. For more detailed information, see our guide on contributing if you're interested in getting involved.
The DLR project Environmental and Crisis Information System (the German abbreviation is UKIS, standing for Umwelt- und Kriseninformationssysteme aims at harmonizing the development of information systems at the German Remote Sensing Data Center (DFD) and setting up a framework of modularized and generalized software components.
UKIS is intended to ease and standardize the process of setting up specific information systems and thus bridging the gap from EO product generation and information fusion to the delivery of products and information to end users.
Furthermore, the intention is to save and broaden know-how that was and is invested and earned in the development of information systems and components in several ongoing and future DFD projects.