Search Results for author: Stefano Puliti

Found 8 papers, 2 papers with code

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

no code implementations28 Jan 2024 Maciej Wielgosz, Stefano Puliti, Binbin Xiang, Konrad Schindler, Rasmus Astrup

In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests.

Instance Segmentation Segmentation +1

Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

1 code implementation22 Dec 2023 Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.

Segmentation

FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees

no code implementations3 Sep 2023 Stefano Puliti, Grant Pearse, Peter Surový, Luke Wallace, Markus Hollaus, Maciej Wielgosz, Rasmus Astrup

In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.

Benchmarking Instance Segmentation +2

Towards accurate instance segmentation in large-scale LiDAR point clouds

1 code implementation6 Jul 2023 Binbin Xiang, Torben Peters, Theodora Kontogianni, Frawa Vetterli, Stefano Puliti, Rasmus Astrup, Konrad Schindler

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances.

Clustering Instance Segmentation +5

AI applications in forest monitoring need remote sensing benchmark datasets

no code implementations20 Dec 2022 Emily R. Lines, Matt Allen, Carlos Cabo, Kim Calders, Amandine Debus, Stuart W. D. Grieve, Milto Miltiadou, Adam Noach, Harry J. F. Owen, Stefano Puliti

We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data.

Benchmarking

National mapping and estimation of forest area by dominant tree species using Sentinel-2 data

no code implementations16 Apr 2020 Johannes Breidenbach, Lars T. Waser, Misganu Debella-Gilo, Johannes Schumacher, Johannes Rahlf, Marius Hauglin, Stefano Puliti, Rasmus Astrup

However, even for municipalities with a decent number of NFI plots, direct NFI estimates were sometimes more precise than MA estimates.

Applications

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