Graph Neural Networks for Crop Cover Mapping

Crop maps provide valuable insights for a range of applications, including water resources management, crop yield prediction, and the planning of domestic and foreign policies. Information on seasonal or yearly agricultural land cover can help governments and organizations make informed decisions to address agricultural challenges and promote environmental sustainability. However, large-scale land cover mapping remains a significant challenge due to the high computational demands of processing remote sensing data, especially when using high-resolution imagery for large-scale applications such as country-wide mapping.

This high computational requirement can be partially mitigated by performing object-based classification, where data is summarized into segments or fields. A challenge in object-level mapping is selecting an appropriate method for analyzing and interpreting the data. For instance, convolutional neural networks (CNNs), a commonly used deep learning algorithm, are not directly applicable in their basic form because they require a gridded structure. Graph Neural Networks (GNNs) present a novel approach, effectively analyzing relationships between objects represented as nodes and edges in a graph. The ability of GNNs to capture complex relationships between segments in a non-grid structure offers distinct advantages, such as handling irregular or non-Euclidean data and exploiting spatial and temporal dependencies within a region. This makes GNNs particularly well-suited for high-resolution remote sensing tasks where traditional grid-based methods may struggle with spatial context and object interactions.

This study applies GNNs to multitemporal Sentinel-1 Interferometric Wide Swath data (VV and VH polarizations), leveraging ten-day composites from May to September to capture seasonal crop growth dynamics. Training, testing, and validation datasets cover 40×40 km², 20×20 km², and 20×20 km² areas, respectively, within North Rhine-Westphalia, Germany. Sentinel-1 images are segmented using the Felzenszwalb-Huttenlocher algorithm, grouping pixels into objects. Each segment’s average backscatter values are calculated, and crop class labels are assigned using InVeKos ground truth data, which includes field boundaries and crop information. This data is transformed into a graph, where nodes represent segments, and edges define adjacency. The GraphSAGE framework is employed to train the GNN model.

Performance comparisons include segment-level and pixel-level neural networks (NNs). Preliminary results show that GNNs achieved the highest accuracy (88.01%), outperforming segment-level NN (86.02%) and pixel-level NN (78.89%). GNNs also demonstrated efficient computational performance, with shorter inference times (0.19 seconds) compared to pixel-based methods (10.7 seconds), and generated more homogeneous maps, minimizing the salt-and-pepper effect.

These results highlight the potential of GNNs for scalable, object-based mapping at high resolution. The approach will be expanded to classify cropland across Germany, generating a 10-meter spatial resolution crop map. By leveraging the temporal dynamics of Sentinel-1 data and incorporating 2022 data, this method offers an efficient and robust framework for large-scale applications in crop management, land-use monitoring, and resource planning.