SEMFIRE Datasets (Forest environment dataset) These datasets are used for semantic segmentation and data augmentation and contain various forestry scenes. They were collected as part of the research work conducted by the Institute of Systems and Robotics, University of Coimbra team within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-032691) research project coordinated by Ingeniarius Ltd. The semantic segmentation algorithms attempt to identify various semantic classes (e.g. background, live flammable materials, trunks, canopies etc.) in the images of the datasets. The datasets include diverse image types, e.g. original camera images and their labeled images. In total the SEMFIRE datasets include about 1700 image pairs. Each dataset includes corresponding .bag files. To launch those .bag files on your ROS environment, use the instructions on the following Github repository Description of each dataset: 2019_2020_quinta_do_bolao_coimbra: Robot moving on a path through a forest environment 2020_ctcv_parking_lot_coimbra: Robot moving in a circle in a parking lot for testings 2020_sete_fontes_forest: A set of forest images acquired by hand-held apparatus Each dataset consists of following directories: images directory: diverse image types, e.g. original camera images and their labeled images rosbags directory: .bag files, which correspond to the image directory Each images directory consists of following directories: img: original camera images lbl: single channel images (ground truth) with corresponding labels for each image in img lbl_colored: camera images in lbl colorized according to different semantic classes (for more details see the datasets descriptions) lbl_overlaid: camera images in img overlaid with corresponding labels (colored) Each rosbags directory contains .bag files with the following topics: 2019_2020_quinta_do_bolao_coimbra_rosbags: /back_lslidar_packet /dalsa_camera_720p/compressed /flir_ax8/compressed /front_lslidar_packet /gps_fix /gps_time /gps_vel /imu/data /realsense/aligned_depth_to_color/image_raw /realsense/color/camera_info /realsense/color/image_raw/compressed /realsense/depth/camera_info /realsense/depth/image_rect_raw/compressed /realsense/extrinsics/depth_to_color 2020_ctcv_parking_lot_coimbra_rosbags: /dalsa_camera_720p/compressed /gps_fix /gps_ime /fused_point_cloud /imu/data /imu/mag /imu/rpy 2020_sete_fontes_forest_rosbags: /realsense/camera_info /realsense/depth_compressed/compressedDepth /realsense/nir/left/compressed /realsense/nir/right/compressed /realsense/rgb/compressed All datasets include a detailed description as a text file. In addition, they include a rosbag_info.txt file with a description for each ROS inside the .bag files as well as a description for each ROS topic. The following table shows the statistical description of typical portuguese woodland configurations with structured plantations of Pinus pinaster (Pp, pine trees) and Eucalyptus globulus (Eg, eucalyptus). "Low density" structured plantation "High density" structured plantation Tree density (assuming plantation in rows spaced 3m apart in all cases) Eg: 900 trees/ha Pp: 450 trees/ha Eg: 1400 trees/ha Pp: 1250 trees/ha Average heights and corresponding ages of plantation trees Eg: 12m (6 years old) Pp: 10m (15 years old) Eg: 12m (6 years old) Pp: 10m (15 years old) Maximum heights and corresponding fully-matured ages of plantation trees Eg: 20m (11 years old) Pp: 30m (40 years old) Eg: 20m (11 years old) Pp: 30m (40 years old) Diameter at chest level (DCL – 1,3m) of plantation trees (average/maximum) Eg: 15cm/25cm Pp: 20cm/50cm Eg: 15cm/25cm Pp: 20cm/50cm Natural density of herbaceous plants 30% of woodland area 30% of woodland area Natural density of bush and shrubbery 30% of woodland area 30% of woodland area Natural density of arboreal plants (not part of plantation) 5% of woodland area 5% of woodland area

This work was supported by the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269) research project co-funded by the "Agência Nacional de Inovação" within the Portugal 2020 programme.