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},
    number = { },
    pages = { },
    title = {Optimal Path Planning for Kinematic Laser Scanning},
    volume = { },
    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},
    number = { },
    pages = { },
    title = {Building Footprint Aggregation with Preservation of Edge Orientations},
    volume = { },
    year = {2026}
}

Historical maps represent an invaluable memory which should be preserved. Such kind of maps are, however, mostly scanned and stored as raster graphics which do not contain semantic information in a machine-readable form. To achieve a machine-readable state, an often expensive human intervention is needed in a fully manual or semi-automatic fashion. An automatic interpretation and a feature extraction is then inevitable for a map digitization and vectorization. Automatic approaches showed more and more convincing and promising results on challenging map corpora avoiding human interaction. This paper deals with the semantic segmentation of historical maps based on Graph Convolutional Networks (GCNs) to capture long-range dependencies between image features. This allows for an extension of the receptive field of Convolutional Neural Networks (CNNs) restricted on local dependencies. A Self-Constructing Graph (SCG) module has been applied to automatically induce the structure of the GCN. We performed experiments revealing promising results where our approach achieved an Mean Intersection over Union (mIoU) of 0.68, outperforming a state-of-the-art CNN dedicated to the semantic segmentation of historical maps.

doi:10.1080/15230406.2025.2468304

@article{arzoumanidis2026semSegHistMaps,
    abstract = {Historical maps represent an invaluable memory which should be preserved. Such kind of maps are, however, mostly scanned and stored as raster graphics which do not contain semantic information in a machine-readable form. To achieve a machine-readable state, an often expensive human intervention is needed in a fully manual or semi-automatic fashion. An automatic interpretation and a feature extraction is then inevitable for a map digitization and vectorization. Automatic approaches showed more and more convincing and promising results on challenging map corpora avoiding human interaction. This paper deals with the semantic segmentation of historical maps based on Graph Convolutional Networks (GCNs) to capture long-range dependencies between image features. This allows for an extension of the receptive field of Convolutional Neural Networks (CNNs) restricted on local dependencies. A Self-Constructing Graph (SCG) module has been applied to automatically induce the structure of the GCN. We performed experiments revealing promising results where our approach achieved an Mean Intersection over Union (mIoU) of 0.68, outperforming a state-of-the-art CNN dedicated to the semantic segmentation of historical maps.},
    author = {Lukas Arzoumanidis and Julius Knechtel and Jan-Henrik Haunert and Youness Dehbi},
    doi = {10.1080/15230406.2025.2468304},
    journal = {Cartography and Geographic Information Science},
    number = {2},
    pages = {177--187},
    title = {Semantic segmentation of historical maps using Self-Constructing Graph Convolutional Networks},
    url = {https://www.tandfonline.com/doi/full/10.1080/15230406.2025.2468304},
    volume = {53},
    year = {2026}
}