Field Boundary Change Detection

Hummingbird Technologies, in collaboration with the STFC's Hartree Centre, are proposing to build a fully automated AI tool for agricultural field boundary detection and change detection that is both commercially viable on a global scale and robust enough to tackle the differing geological, land use and farming variances encountered in agriculture.

The project will build on Hummingbird's existing R&D projects, with the Hartree Centre contributing to critical knowledge gaps in deep neural networks and high performance computing.

The challenge is twofold:

First is a static challenge - defining the boundary. This will not use any prior information but will aim to train a model that recognises field boundaries across a defined region or, alternatively, locally after a single-click inside a field. To ensure we are able to do this on a global scale, the land management practices that are implemented in different geographies need to be taken into account as well as man made features that are only common in a specific locality. To ensure we are producing something that can do this accurately, we need to identify and define each feature within the dataset used by the model.

Second is dynamic - using prior boundary information we will develop a deep learning algorithm that runs periodically over time to identify areas which are subject to land use change. This algorithm will need to make use of large satellite image datasets that contain many samples of fields which are each defined by different features, aiming to robustly predict both geometric change of the boundaries and land use changes. Detection over time will also contain significant noise and calibration issues when capturing imagery on different dates, and these data acquisition issues will need to be discriminated from actual changes. A thorough validation of the tool will need to be performed through a continuous process so that we can measure model performance and ensure we are providing the correct results to customers.