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

Informazioni relative al progetto

GeoAI_LULC_Seg

ID dell’accordo di sovvenzione: 101100837

DOI

10.3030/101100837

Progetto chiuso

Data della firma CE 22 Settembre 2022

Data di avvio 1 Ottobre 2022

Data di completamento 31 Marzo 2024

Finanziato da

• European Research Council (ERC)

Costo totale

Nessun dato

Contributo UE

€ 150 000,00

Coordinato da

KOC UNIVERSITY
Türkiye

Periodic Reporting for period 1 - 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)

Periodo di rendicontazione: 2022-10-01 al 2024-03-31

Sintesi del contesto e degli obiettivi generali del progetto

This project aimed to develop geospatial artificial intelligence (GeoAI) processes to semantically segment panchromatic historical aerial and satellite imagery at an unprecedented scale for a border region shared between the Republic of Bulgaria and the Republic of Türkiye. Our goal was to create accurate land use and land cover change maps for this region and align them with high spatial resolution demographic data. This alignment would enable us to assess, quantify, and analyze major drivers of land use land cover changes such as rural depopulation, agricultural land abandonment, and deforestation over the long term. Additionally, we aimed to predict and model future population geography and land use and land cover changes.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

Despite limited budgetary and time constraints, we successfully developed our GeoAI methodology tailored for the semantic segmentation of panchromatic historical aerial and satellite imagery. We ensured its interoperability with the current land cover taxonomy of the European Space Agency (ESA) WorldCover 2021. This key achievement enabled us to conduct long-term land cover change analyses in our chosen area.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

Our GeoAI methodology and deep learning processes have demonstrated high accuracy in computer vision. More significantly, they enabled us to analyze historical aerial and satellite imagery alongside historical demographic data from an interdisciplinary viewpoint, integrating remote sensing, population geography, and geospatial data science. A major outcome of this project is the scalability of our methodology, showcased by the semantic segmentation and public sharing of a benchmark land cover dataset for KeyHole-9 Hexagon reconnaissance satellite imagery from the 1970s and the 1980s, which provides nearly global high-resolution coverage. This Hexagon imagery was classified and made accessible online as recently as 2020 and 2022. As a result, we believe our methodology is both valuable and timely for future research.

In addition, our benchmark HexaLCSeg dataset (Hexagon-based Historical Land Cover Benchmark Dataset, https://zenodo.org/records/11005344) derived from Hexagon satellite imagery, offers significant potential for various applications. It is particularly useful for environmental and agricultural monitoring and urban planning, as it provides insights into historical land cover conditions and long-term changes such as urbanization, deforestation, and land abandonment. The historical perspective provided by the HexaLCSeg dataset makes it an essential tool for climate change research, allowing the tracking of ecological changes over decades. Furthermore, from the standpoint of geohumanities and human geography, the spatiotemporal characteristics of Hexagon imagery, including urban sprawl in Türkiye and significant rural depopulation in Bulgaria, are invaluable for analyzing and modeling the major dynamics of past and future population geography.