Data

CS-Wild-Places Dataset

Commonwealth Scientific and Industrial Research Organisation
Griffiths, Ethan ; Haghighat, Maryam ; Denman, Simon ; Fookes, Clinton ; Ramezani, Milad
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/6mde-j845&rft.title=CS-Wild-Places Dataset&rft.identifier=https://doi.org/10.25919/6mde-j845&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=CS-Wild-Places is a large-scale lidar dataset for cross-source place recognition between ground and aerial viewpoints in forest environments. The data was collected from four forests in Brisbane, Australia over ten months, with lidar scans captured by a handheld sensor payload (below canopy) and aerial drone (above canopy). CS-Wild-Places builds upon the Wild-Places dataset by introducing geo-registered aerial submaps covering Karawatha and Venman forests, enabling research into place recognition between challenging viewpoints. We further release ground and aerial data captured in two new forest environments: QCAT and Samford Ecological Research Facility. The aerial data spans 370 hectares in total, and we captured two ground sequences with 3.5km of total traversal, producing a total of ~36k high resolution lidar submaps with accurate 6-DoF poses and timestamps. We release the data in three main configurations: raw (submaps randomly downsampled to 500k points max), and post-processed (submaps voxel-downsampled with 0.8m or 0.4m voxels, ground points removed, with and without normalisation).Lineage: The ground data was collected with a handheld sensor payload consisting of a VLP-16 lidar sensor spinning at a 45 degree angle to maximise field of view, an IMU, GPS antenna, and four cameras. For each collected sequence we use Wildcat SLAM to register the lidar data into a globally consistent map with accurate 6-DoF pose estimation, from which we produce our submaps by collecting points within a 2-second sliding window every 2 seconds along the sensor trajectory.The aerial data is collected with two setups. For Karawatha, Venman, and QCAT, we used a DJI M300 quadcopter equipped with a VLP-32C lidar sensor. For Samford, we used an Acecore NOA hexacopter equipped with a RIEGL VUX-120 pushbroom lidar. Aerial global maps are geo-registered using Wildcat SLAM with GPS RTK, and further aligned with the ground global maps using ICP. Aerial submaps are uniformly sampled from a 10m-spaced grid spanning the aerial map. All submaps are stored in the local coordinate frame, and 6-DoF poses are stored in UTM coordinates.&rft.creator=Griffiths, Ethan &rft.creator=Haghighat, Maryam &rft.creator=Denman, Simon &rft.creator=Fookes, Clinton &rft.creator=Ramezani, Milad &rft.date=2026&rft.edition=v5&rft.relation=https://doi.org/10.48550/arXiv.2503.08140&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2025.&rft_subject=Lidar&rft_subject=Place Recognition&rft_subject=Dataset&rft_subject=Natural&rft_subject=Forest&rft_subject=Aerial&rft_subject=AI&rft_subject=Machine Learning&rft_subject=Robotics&rft_subject=Autonomous vehicle systems&rft_subject=Control engineering, mechatronics and robotics&rft_subject=ENGINEERING&rft_subject=Autonomous agents and multiagent systems&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Machine learning not elsewhere classified&rft_subject=Machine learning&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2025.

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CS-Wild-Places is a large-scale lidar dataset for cross-source place recognition between ground and aerial viewpoints in forest environments. The data was collected from four forests in Brisbane, Australia over ten months, with lidar scans captured by a handheld sensor payload (below canopy) and aerial drone (above canopy). CS-Wild-Places builds upon the Wild-Places dataset by introducing geo-registered aerial submaps covering Karawatha and Venman forests, enabling research into place recognition between challenging viewpoints. We further release ground and aerial data captured in two new forest environments: QCAT and Samford Ecological Research Facility. The aerial data spans 370 hectares in total, and we captured two ground sequences with 3.5km of total traversal, producing a total of ~36k high resolution lidar submaps with accurate 6-DoF poses and timestamps. We release the data in three main configurations: raw (submaps randomly downsampled to 500k points max), and post-processed (submaps voxel-downsampled with 0.8m or 0.4m voxels, ground points removed, with and without normalisation).
Lineage: The ground data was collected with a handheld sensor payload consisting of a VLP-16 lidar sensor spinning at a 45 degree angle to maximise field of view, an IMU, GPS antenna, and four cameras. For each collected sequence we use Wildcat SLAM to register the lidar data into a globally consistent map with accurate 6-DoF pose estimation, from which we produce our submaps by collecting points within a 2-second sliding window every 2 seconds along the sensor trajectory.

The aerial data is collected with two setups. For Karawatha, Venman, and QCAT, we used a DJI M300 quadcopter equipped with a VLP-32C lidar sensor. For Samford, we used an Acecore NOA hexacopter equipped with a RIEGL VUX-120 pushbroom lidar. Aerial global maps are geo-registered using Wildcat SLAM with GPS RTK, and further aligned with the ground global maps using ICP. Aerial submaps are uniformly sampled from a 10m-spaced grid spanning the aerial map. All submaps are stored in the local coordinate frame, and 6-DoF poses are stored in UTM coordinates.

Available: 2026-02-06

Data time period: 2023-09-07 to 2024-07-10

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Subjects
AI | Aerial | Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Autonomous Agents and Multiagent Systems | Autonomous Vehicle Systems | Control Engineering, Mechatronics and Robotics | Dataset | Engineering | Forest | Information and Computing Sciences | Lidar | Machine Learning | Machine Learning | Machine Learning Not Elsewhere Classified | Natural | Place Recognition | Robotics |

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Identifiers
ACN 633 798 857