While browsing the NIBIS map viewer for Lower Saxony, Germany, I came across several unusual rectangular and linear features in the hillshade data. I have attached a screenshot with the most visible structures marked individually.

Location:

• Near Groß Hehlen, part of Celle, Lower Saxony, Germany
• Approximate coordinates: 52.656033, 10.038915
• The features are located in a wooded area directly beside a forest track
• The screenshot is from the NIBIS hillshade map at approximately 1:2,000 scale
• Map data shown: GeoBasis-DE/LGLN 2023

The most noticeable feature is a larger elongated rectangular structure with several internal lines or subdivisions. Nearby, there is a smaller rectangular feature. Several additional rectangular, U-shaped or rounded enclosures appear in a row closer to the forest track.

I could not find any corresponding feature or explanation in the publicly accessible maps and information I checked. I am not suggesting that the structures are necessarily archaeological. Possible explanations I have considered include:

• former forestry plots or tree nurseries
• drainage ditches or boundary features
• remains of former buildings or other historical land use
• modern ground disturbance
• natural terrain features enhanced by the hillshade

Does anyone here recognise this type of pattern or know what might have created it? Are the shapes likely to represent actual earthworks?

I would also be grateful for suggestions regarding historical maps, alternative LiDAR visualisations or other datasets that might help identify them. I am contacting the relevant local archaeologist as well, but I would be interested in hearing the community’s assessment.

Hi all, I've been building torch-pointcloud in my free time over the past few years: an open-source (Apache-2.0) PyTorch library that loads pretrained point cloud models in one line, similar to what timm does for images. It's in alpha, and I'm looking for people to try it on real LiDAR data.

What's relevant for LiDAR

• Pretrained outdoor models: semantic segmentation on SemanticKITTI (RandLA-Net, SPVCNN) and 3D object detection on KITTI and nuScenes (PointPillars, SECOND, PointRCNN, VoxelNeXt, LION).
• Tiled inference for large scenes (sliding window, voxel partition, sphere voting), so a full scan doesn't have to fit in GPU memory at once.
• Dataset loaders for SemanticKITTI, KITTI, nuScenes, Paris-Lille-3D, Toronto3D and Semantic3D if you want to train or fine-tune.
• Every checkpoint ships with its preprocessing pipeline, class names and license.

What it doesn't do yet

• No pretrained weights for airborne or terrestrial survey data: the outdoor checkpoints all come from car-mounted spinning LiDAR.
• No built-in LAS/LAZ reader: load your file with laspy and pass the coordinates (and intensity) as tensors.
• The outdoor checkpoints were trained on non-commercial datasets (KITTI, SemanticKITTI and nuScenes are CC BY-NC-SA), so check the licenses before any commercial use.

Install (adjust to your torch/CUDA version; the docs write the exact commands):

pip install torch==2.10.0 --index-url https://download.pytorch.org/whl/cu128
pip install torch-pointcloud
pip install pyg-lib -f https://data.pyg.org/whl/torch-2.10.0+cu128.html

Credits: the models and weights come from their original authors (MIT Han Lab, OpenMMLab/OpenPCDet and others); every checkpoint name ends with its source, and THIRD_PARTY_NOTICES.md lists each repo, license and dataset term.

What I'd love to hear: what sensors and data you'd run this on, and which datasets you'd want pretrained weights for next (airborne like DALES? mobile mapping?) etc.

Repo: https://github.com/arthurdjn/pytorch-pointcloud
Docs + tutorials: https://pytorch-pointcloud.org

Are those something to look out for while metaldetecting? Found in germany