Teemu Hakala

Julius Pesonen, Teemu Hakala, Väinö Karjalainen et al.

Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment requiring minimal infrastructure and equipping them with small cameras and computers enables autonomous real-time detection. In remote areas, however, detection methods are limited to onboard computation due to the lack of high-bandwidth mobile networks. For accurate camera-based localisation, segmentation of the detected smoke is essential but training data for deep learning-based wildfire smoke segmentation is limited. This study shows how small specialised segmentation models can be trained using only bounding box labels, leveraging zero-shot foundation model supervision. The method offers the advantages of needing only fairly easily obtainable bounding box labels and requiring training solely for the smaller student network. The proposed method achieved 63.3% mIoU on a manually annotated and diverse wildfire dataset. The used model can perform in real-time at ~25 fps with a UAV-carried NVIDIA Jetson Orin NX computer while reliably recognising smoke, as demonstrated at real-world forest burning events. Code is available at: https://gitlab.com/fgi_nls/public/wildfire-real-time-segmentation

Lassi Ruoppa, Tarmo Hietala, Verneri Seppänen et al.

Individual tree segmentation (ITS) from LiDAR point clouds is fundamental for applications such as forest inventory, carbon monitoring and biodiversity assessment. Traditionally, ITS has been achieved with unsupervised geometry-based algorithms, while more recent advances have shifted toward supervised deep learning (DL). In the past, progress in method development was hindered by the lack of large-scale benchmark datasets, and the availability of novel data formats, particularly multispectral (MS) LiDAR, remains limited to this day, despite evidence that MS reflectance can improve the accuracy of ITS. This study introduces FGI-EMIT, the first large-scale MS airborne laser scanning benchmark dataset for ITS. Captured at wavelengths 532, 905, and 1,550 nm, the dataset consists of 1,561 manually annotated trees, with a particular focus on small understory trees. Using FGI-EMIT, we comprehensively benchmarked four conventional unsupervised algorithms and four supervised DL approaches. Hyperparameters of unsupervised methods were optimized using a Bayesian approach, while DL models were trained from scratch. Among the unsupervised methods, Treeiso achieved the highest test set F1-score of 52.7%. The DL approaches performed significantly better overall, with the best model, ForestFormer3D, attaining an F1-score of 73.3%. The most significant difference was observed in understory trees, where ForestFormer3D exceeded Treeiso by 25.9 percentage points. An ablation study demonstrated that current DL-based approaches generally fail to leverage MS reflectance information when it is provided as additional input features, although single channel reflectance can improve accuracy marginally, especially for understory trees. A performance analysis across point densities further showed that DL methods consistently remain superior to unsupervised algorithms, even at densities as low as 10 points/m$^2$.

Oona Oinonen, Lassi Ruoppa, Josef Taher et al.

The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the use of drones. Current challenges in data processing are related to the limited spectral information and low point density of most ALS datasets. Another challenge will be the growing need for annotated training data, frequently produced by manual processes, to enable semantic interpretation of point clouds. This study proposes to semantically segment new high-density (1200 points per square metre on average) multispectral ALS data with an unsupervised ground-aware deep clustering method GroupSP inspired by the unsupervised GrowSP algorithm. GroupSP divides the scene into superpoints as a preprocessing step. The neural network is trained iteratively by grouping the superpoints and using the grouping assignments as pseudo-labels. The predictions for the unseen data are given by over-segmenting the test set and mapping the predicted classes into ground truth classes manually or with automated majority voting. GroupSP obtained an overall accuracy (oAcc) of 97% and a mean intersection over union (mIoU) of 80%. When compared to other unsupervised semantic segmentation methods, GroupSP outperformed GrowSP and non-deep K-means. However, a supervised random forest classifier outperformed GroupSP. The labelling efforts in GroupSP can be minimal; it was shown, that the GroupSP can semantically segment seven urban classes (building, high vegetation, low vegetation, asphalt, rock, football field, and gravel) with oAcc of 95% and mIoU of 75% using only 0.004% of the available annotated points in the mapping assignment. Finally, the multispectral information was examined; adding each new spectral channel improved the mIoU. Additionally, echo deviation was valuable, especially when distinguishing ground-level classes.