2025-04-08, 18:20–18:24, W - Invite
The advent of modern satellite constellations–combined with significant advances in computational technology and resources–enables the systematic mapping of agricultural areas in high spatiotemporal resolution and through this provides a foundational building-block for the study and monitoring of soil health. In this context, parts of the Earth Observation and Machine Learning community have placed a growing focus on the development of capabilities to remotely identify crop types growing on agricultural cropland. These capabilities can support soil health monitoring efforts by providing systematic insights into crop rotations and cropping practices that affect soil health (e.g. cover crops). End users of these capabilities desire high accuracy across large areas with diverse agro-environmental conditions, as early in the season as possible.
Whilst recent efforts have typically shown strong performance on datasets that express limited spatial and/or temporal variability, the community is yet to explore performance across an adequate number of years at the pan-European scale to assess robustness both to the spatial variations between environmental & political zones and to the full range of variations in interannual weather patterns. To do so, there is still a need for datasets that provide sufficient spatial and temporal depth.
In Europe, the public release of historical LPIS and GSAA datasets by EU member states at either regional or national scales has made high quality crop type ground-truth data more available than ever before. The RapidCrops dataset combines these datasets across a range of countries and multiple years to provide a deep spatio-temporal stack of crop type ground-truth data; enabling assessments of generalization across both space and time. The dataset leverages harmonization standards developed under the EuroCrops initiative to standardize data from different countries. It adopts & extends the fiboa (https://fiboa.org) field boundaries data standard to make parcel boundaries and crop type labels available in an open, interoperable format. Finally, the dataset provides additional usability metrics for each parcel to help users identify and access the data most appropriate to their context, quickly.
As a Senior Scientist in the Solutions Enablement group at Planet Labs, Piers works at the intersection of earth observation (EO) and machine learning to solve problems in the agricultural monitoring domain. His research interests include the development of EO-ML data infrastructure, and applications that leverage EO time series or the fusion of multiple EO modalities.
Annett Wania holds a PhD in Geography and has 21 years of experience in using geospatial and Earth Observation data for the analysis of the impact of human activities. After obtaining her PhD in Geography from the University of Strasbourg, France, she has been working at the European Commission’s Joint Research Centre for 13 years on applications in the environmental and agricultural domain as well as applications on urban environments and disaster management. During the last six years at JRC she was working on satellite-based mapping for disaster management under the Copernicus Emergency Management Service. During her time at the JRC she has transitioned from conducting technical work to managing scientific and technical projects and teams. Since January 2021 she is working at Planet Labs in the Earth Observation Lab, where she is managing a team of six engineers and data scientists which is implementing a number of research and development projects aiming at testing and further developing Planet’s image products for applications in the environmental and agricultural domain (crop classification, phenology, environmental impact of mining activities). In addition to traditional remote sensing methods, the team’s focus is on experimenting with innovative machine learning techniques to extract information from Planet’s high-cadence imagery and multi-modal datasets and define solutions for customers, which help them build new applications based on Planet data.