KfW supports the development of the Open Source R package mapme.biodiversity. The package is written in R and published on CRAN. It is used by analysts to process big geospatial data efficiently. A major upgrade is currently being worked on to enable seamless scaling of workflows from laptops to the cloud for anyone. For this, the GDAL library which provides an efficient format-agnostic abstraction layer to access geospatial data from a variety of sources has been leveraged. Together with a standardized output format, the package delivers crucial information in a timely manner for evidence-based decision making in development cooperation projects. This work on this software has been presented at the General Assembly of the European Geosciences Union (EGU) 2024. In a session dedicated to the usage of Earth Observation to monitor and achieve the UN Sustainable Development Goals (SDG), the software was showcased for its delivery of value in a fast, reproducible, and transparent way. You can find the slides of the presentation here. The planned release date of the upgrades is planned for the end of June 2024. You can check a dedicated issue on our GitHub repository to stay informed of the progress.
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We are thrilled to introduce you to the next chapter in our journey: the technical relaunch of our MAPME website.
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Going beyond deforestation statistics in conservation evaluation: How landscape metrics can help us to understand the effects of infrastructure development on habitat integrity.
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Going beyond deforestation statistics in conservation evaluation: How landscape metrics can help us to understand the effects of infrastructure development on habitat integrity.
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How satellite Earth observation and in-situ data help to monitor agricultural production in the Senegal Delta.
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How Earth observation data helps to support combating invasive species Prosopis juliflora in the Afar Region, Ethiopia.
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How data helps us to measure our project impacts and to identify future challenges in and around protected areas.
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How satellite data can help us understand spatial patterns of agricultural productivity through time.
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