Establishing sustainable agricultural systems while ensuring food security has become a global priority. Meeting this goal requires contributions from different fields of agricultural science, many of which depend on detailed information on crops. Recent advancements in deep learning and the transnational harmonization of administrative data have led to the availability of ever-larger datasets of agricultural field polygons. These datasets, however, vary in quality and level of detail. To achieve synergies between different information sources through data fusion and to evaluate the quality of model outputs, it is essential to efficiently identify correspondences in spatially overlapping datasets. We address this challenge by leveraging a state-of-the-art matching algorithm that we adapt by redesigning its connected-component decomposition to handle large-scale datasets of agricultural field polygons. We demonstrate the algorithm’s suitability through two case studies. First, we show how automatically delineated field polygons can be validated against ground truth in terms of their spatial quality. Second, we explore how two established reference datasets align both thematically and spatially. We discuss the dataset comparisons using different evaluation metrics and provide an interactive map viewer that enables the exploration of spatial patterns of the datasets’ alignment by visualizing matching qualities in the geographic context.

doi:10.1080/17538947.2026.2632420

@article{naumann2026aggMatching,
    abstract = {Establishing sustainable agricultural systems while ensuring food security has become a global priority. Meeting this goal requires contributions from different fields of agricultural science, many of which depend on detailed information on crops. Recent advancements in deep learning and the transnational harmonization of administrative data have led to the availability of ever-larger datasets of agricultural field polygons. These datasets, however, vary in quality and level of detail. To achieve synergies between different information sources through data fusion and to evaluate the quality of model outputs, it is essential to efficiently identify correspondences in spatially overlapping datasets. We address this challenge by leveraging a state-of-the-art matching algorithm that we adapt by redesigning its connected-component decomposition to handle large-scale datasets of agricultural field polygons.
We demonstrate the algorithm’s suitability through two case studies. First, we show how automatically delineated field polygons can be validated against ground truth in terms of their spatial quality. Second, we explore how two established reference datasets align both thematically and spatially.
We discuss the dataset comparisons using different evaluation metrics and provide an interactive map viewer that enables the exploration of spatial patterns of the datasets’ alignment by visualizing matching qualities in the geographic context.},
    author = {Naumann, Alexander and Gedicke, Sven and Haunert, Jan-Henrik},
    doi = {10.1080/17538947.2026.2632420},
    journal = {International Journal of Digital Earth},
    number = {1},
    pages = {2632420},
    title = {A {S}calable {M}atching {A}pproach for the {C}omparison of {A}gricultural {L}and {U}se {M}aps {B}ased on {C}orresponding {F}ield {P}olygons},
    volume = {19},
    year = {2026}
}

@article{knechtel2026kinematicScanPlanning,
    author = {Knechtel, Julius and Kordgholiabad, Mohammad and Haunert, Jan-Henrik},
    journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    note = {Accepted for publication, ISPRS Congress 2026},
    title = {Optimal Path Planning for Kinematic Laser Scanning},
    year = {2026}
}

@article{naumann2026footprintAggEdgeOrientation,
    author = {Naumann, Alexander and Bergé, Samuel and Sauer, Jonas and Haunert, Jan-Henrik},
    journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    note = {Accepted for publication, ISPRS Congress 2026},
    title = {Building Footprint Aggregation with Preservation of Edge Orientations},
    year = {2026}
}