Record #337350 - GSI Repository

GeoAI_LULC_Seg

A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040

Grant period	2022-10-01 - 2024-03-31
Funding body	European Union
Call number	ERC-2022-POC2
Grant number	101100837
Identifier	G:(EU-Grant)101100837

Note: Rural depopulation, agricultural land abandonment, and deforestation are massive concerns for Europe and elsewhere today and our planet's future. These interlinked phenomena can be analysed using land use and land cover (LULC) maps combined with dynamics of population geography, especially regarding urban sprawl. Modern LULC and spatially disaggregated population datasets go back to the 1980s and 1970s. Although we have earlier population data, these are not geomatched to locations in LULC maps. Earlier LULC maps are either not very reliable (extracted from historical maps) or limited in their geographical coverage (based on selected aerial photos or satellite imagery). These are severe limitations to developing longer and deeper perspectives and understanding the root causes of these detrimental changes in population geography and land use practices in large territories. GeoAI_LULC_Seg will develop an advanced, modular, and customizable geospatial artificial intelligence-based land use land cover segmentation process to accurately map LULC conditions for around 30,000 km2 in a border region between Bulgaria and Turkey, including the cities Edirne, Istanbul, and Plovdiv, from historical aerial photographs and early reconnaissance satellite images (dating back to the 1950s and the 1970s respectively) by pairing them with geotagged historical population census data. Our methodological novelties are not limited to GeoAI-based object segmentation and super-resolution applications for panchromatic imagery for our research area. Our project will create transferable knowledge and scalable methods for global applications for the 1970s, thanks to worldwide coverage of high-spatial-resolution satellite imagery we will process. Furthermore, we will build long-term LULC maps series commensurable with current satellite data (1950-2020), allowing us to improve predictions for future population geography and LULC changes.

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Record created 2023-03-01, last modified 2023-03-01

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