Labelling GIS Data for AI & Deep Learning
- Anvita Shrivastava
- Oct 16
- 4 min read
Within the era of Geospatial AI, high-quality labelled GIS data is the foundation upon which success rests. Even the most sophisticated neural networks will fail without accurate annotation and geo-referencing. This post will examine the techniques, challenges, best practices, and toolchains for labelling GIS data to develop strong AI and deep learning systems.
Why Labelling is Needed in GIS + AI
Deep learning is supervised and therefore requires labelled data as the ground truth — the models learn the mapping from the input (e.g. images, point clouds) to the output labels.
In GIS, labels often represent spatial, spectral, contextual, and semantic information. Poor or inconsistent labels cause the model to generalise poorly.
In geospatial contexts, complexities occur in geospatial data that do not exist in "vanilla" computer vision tasks — georeferencing, coordinate systems, and spatial context.
The synergy between GIS and AI (often referred to as GeoAI) enables automated feature extraction, change detection, land use classification, and object detection (e.g., buildings, roads), all at scale.
Therefore, labelling has more depth than simply drawing boxes — we are generating disciplined and aware ground truth in this sense.
Types of GIS Data & Labelling Targets
Understanding the nature of your data is key to choosing labelling strategies:
Data Type | Labeling Targets | Challenges |
Raster imagery (satellite, aerial, drone) | Semantic segmentation, object detection, instance segmentation | Clouds, shadow, multi-spectral bands, resolution differences |
Vector data (points, lines, polygons) | Assigning classes or attributes to geometry | Ensuring topology correctness, overlapping features |
Point clouds / LiDAR | Classifying points (ground, vegetation, buildings), segmentation | Sparsity, density variation, occlusion, and coordinate noise |
Multi-temporal / time-series GIS | Labelling change events (e.g. deforestation, urban expansion) | Temporal alignment, label drift over time |
Annotating Techniques & Approaches
Below are some of the more common types of techniques in geospatial labelling:
Pixel-level / Semantic Segmentation
Classify every pixel into a specific class (e.g. water, vegetation, building). This can help when applying dense classification.
Polygon / Vector Annotation
Draw polygons around objects of interest (e.g. building footprints, parcels) and convert to a mask or bounding definitions.
Bounding Boxes / Object Detection
For discrete objects (cars, trees), annotate or surround with tight bounding boxes. When possible, try to avoid working with excessive background.
Keypoint / Skeleton Annotation
For linear infrastructure (roads, pipelines), allow annotators to mark the critical nodes or skeletons.
Point-level labeling
Sometimes, you will just mark sample points (e.g. sample points for the classes of land cover).
Labelling Toolchains & Frameworks
Presented here is a preliminary, loose and incomplete collection of tools and platforms for GIS labelling and application for artificial-intelligence applications:
Geo-spatial Cloud Labelling Platforms (e.g., GeoWGS84.ai) are web-based tools for geospatial annotation that typically require minimal customisation to efficiently label geospatial data formats like GeoTIFF, MrSID, or JPEG2000.
ArcGIS Pro— includes built-in capabilities via “Label Objects for Deep Learning” pane, export tools for training, and is linked to spatial workflows.
QGIS + plugins/custom tools —an open-source version of ArcGIS; has plugins for annotation support, shapefile editing and raster clip.
Custom Annotation Pipelines / Recipe — utilise python libraries (example: rasterio, GDAL, shapely, geopandas) to preprocess, serve, and validate labels
3D VR labelling tools — (example: as it relates to semantic segmentation for point cloud in 3D environments).
Automated systems for AI-assisted mapping production — for example, Tencent’s HD map annotations use a pipeline to automate map labelling and collections utilising AI + active learning at scale.
Transitioning from Labels to Model Training
Once the annotation is complete, your process normally continues as follows:
Export labels formats - i.e. COCO JSON, Pascal VOC XML, mask PNGs; GeoJSON, or your own format.
Split your data into train/validation/test sets based on ideally spatially disjoint areas to avoid leakage.
Preprocess the data - i.e. normalised, resized, aligned imagery and labels.
Train your models - CNNs, U-Nets, Mask R-CNN, DeepLab, etc.
Inference & post-processing - convert back to geospatial geometry, perform vector cleanup, and smoothing.
Conduct an accuracy assessment - i.e. intersection-over-union (IoU); precision/recall; confusion matrices; and spatial metrics.
Iterate to improve - i.e. discover where error hot spots are; measure the response of finding new training samples; re-label; and retrain.
ArcGIS deep learning tools facilitate integrated and easy steps, such as export, training, and inference steps.
Advanced & Research Directions
Transfer Learning and Domain Adaptation: Expand upon deep learning models that have been pretrained on one location or modality (sensor), or to simply fine-tune networks to new locations.
Self-supervised / unsupervised approaches: Strengthening learning in deep networks while decreasing the need for labelled data by using readily available unlabeled examples.
Weak Supervision: Providing weakly labelled or coarse label (or heuristics) information (e.g., from public land records or cadastral maps) for initialisation of labels.
Generative approaches (e.g. GANs GANs-based initiative to have labels propagated to synthetic training samples).
Multi-modal Annotation of Imagery, LiDAR, Radar, Hyperspectral, etc., for more detailed labels to be provided.
3D Semantic / Volumetric Labelling of Point Clouds, Voxel Grids, or BIM Models.
Active Learning & Human-in-the-Loop pipelines for sampling via reduced manual labelling in large-scale mapping systems.
Interpretability & Explainability of Geospatial Models — Linking the semantics of labels to features learned.
Via one research project, the idea of utilising well-labelled map sample labels for autonomous labelling was explored to help alleviate some of the burden of manual labelling.
Labelling GIS data for AI and deep learning is a foundational, but often overlooked, aspect of creating precise operational geospatial models. It requires domain knowledge, the careful selection of tools, consistency, and a process of quality control. Increasingly, as GeoAI grows, automated and semi-automated labelling processes, active learning strategies, and a hybrid workflow of human and AI will be needed more than ever.
For more information or any questions regarding AI & Deep Learning, please don't hesitate to contact us at
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