Project description
Mapping environmental change through the 1940s
While climate science relies on the constant stream of data from orbital sensors, the most transformative period of human impact on the planet, the post-war Great Acceleration, predates the satellite era. There is a void in our visual understanding of ecological change. Before the launch of Landsat in 1972, the record of how forests were cleared, wetlands drained, and rural infrastructures realigned remained largely fragmented and unquantified. The ERC-funded 3TMAAP aims to study the vast archives of aerial photography dating back to the 1940s. Unlike early satellite imagery, which was often coarse and low-resolution, these historical flight surveys offer a granular view, capturing details as specific as individual hedgerows, watercourses, and field boundaries.
Objective
This project begins with a “book:” eight historical case studies detailing environmental change will serve to produce 8 machine learning training data sets to develop a methodology for the automatic analysis of quantitative and visual environmental data from historical aerial photographs. This will allow for a new approach to study the 1940s-1990s Great Acceleration, which caused unprecedented environmental change. Becoming ubiquitous in the 1940s, aerial photography has a sub-1-meter resolution which is key to identifying non-linear, non-monumental, and pre-industrial rural environmental infrastructure (e.g. trees, waterholes, fields) and how it has changed over time. Since satellite imagery only gained a comparable high resolution in the early 2000s, the ability to machine-read aerial photographs will quadruple the availability of high-resolution geospatial data, thereby allowing a more robust and nuanced analysis of environmental and climate change. The project goals are: (1) to develop a historical ground truthing method to enhance the interpretation of historical aerial photographs to imagine historical landscapes and how they changed; (2) to produce machine deep learning training data sets by deploying historical ground truthing to identify and extract a set of features from sample areas in the study regions; (3) to use a more systematic and more historically informed object-based methodology to machine read archival aerial photography that is deployable at different spatial (local, regional, global) and temporal scales and make it available as an Open Access online software to read historical aerial photography world-wide. By unlocking the mass data contained in archival aerial photography 3TMAAP opens a new path of research by systematically integrating historical ground truthing into the automatization of geospatial analysis, dramatically deepening the time depth of high-resolution mass data for historical analysis and geospatial and climate modelling.
Fields of science (EuroSciVoc)
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Call for proposal
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(opens in new window) ERC-2024-ADG
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69007 Lyon
France
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