Dataset Overview This dataset was developed as part of the research study: Janota et al., "Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives", Computers and Electronics in Agriculture, 2025. DOI: 10.1016/j.compag.2025.111031 Details regarding data collection procedures are available in the paper referenced above. Due to the large volume of data generated during the study, only part of the datasets is publicly released. The remaining datasets can be provided upon reasonable request. The datasets included in this release are: Map Checkpoints: Sets of cells with belief values over considered states, along with a list of all integrated observations in the map. Trained Models: Classification models for egg-laying, open brood, capped brood, and cell detection model. Annotated Cell Sequences: A collection of 531 cell sequences containing individual cell images and corresponding annotations. The code for the paper can be found at: https://gitlab.sensorbees.eu/janota/2025_cea_mapping.git 1. Map Checkpoints The map folder contains an image of the final map, .npz files with the individual map checkpoints and a .csv file with all the integrated observations in the map (open cell, capped brood, egg-laying event). Individual Map Checkpoints The individual .npz files contain the following attributes: Attribute Description centers metric positions of the cells (in meters) counts a number of detections of the cell sizes estimated radius of the cell (in pixels) last_prediction_timestamp UTC timestamp (seconds) of the last prediction step (default=-1 if the cell was not observed yet) last_update_timestamp UTC timestamp (seconds) of the last update step with observation (default=-1 if the cell was not observed yet) cell_states belief over the states (43-dimensional vector of values in range (0, 1)) List of all integrated observations The .csv file with all integrated observations has the following columns: Column name Description cell_id ID of the cell in the map that the observation belongs to det_id -2 for egg-laying obs., -1 for capped brood obs., >= 0 for open cell obs. u_x x-position in image (pixels) for capped brood obs., metric x-position (meters) for open cell obs. v_y_conf y-position in image (pixels) for capped brood obs., metric y-position (meters) for open cell obs., position confidence for egg-laying obs. timestamp UTC timestamp (seconds) of the obs. obs confidence of open brood/capped brood/egg-laying classifier bag_name identifier for the images (not relevant without raw data) scan_id identifier for the images (not relevant without raw data) img_id identifier for the images (not relevant without raw data) 2. Trained Models We provide the following trained models in the folder models: Cell detection (YOLOv5s6) Open brood classification (ResNet-9) Capped brood classification (ResNet-9) Egg-laying event classification (ResNet-9) 3. Annotated Cell Image Sequences We provide both the test and train cell sequence images in respective folders test_sequences_2024, train_sequences_2024. The folders contain subfolders with names denoting the ID of the cells. Each cell folder contains images (both open cell detections and occluded/capped cell images) and a .csv file with the following columns: Column name Description cell_id ID of the cell in the map that the observation belongs to det_id -1 for occluded/capped brood observation, >= 0 for open cell observation x x-position (pixels) of the cell in the corresponding raw image y y-position (pixels) of the cell in the corresponding raw image timestamp UTC timestamp (nanoseconds) when the corresponding image was taken capped_brood 1 if observation is capped brood, 0 otherwise labels -2 for capped brood, -1 for unknown, 0 for other, 1 for any open brood, 2 for egg, 3 for larva bag_name identifier for the images (not relevant without raw data) scan_id identifier for the images (not relevant without raw data) img_id identifier for the images (not relevant without raw data) Citation To attribute this dataset in your research, please cite the corresponding paper: Janota et al., "Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives", Computers and Electronics in Agriculture, 2025. DOI: 10.1016/j.compag.2025.111031