Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Puliti, Stefano"'
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers
Externí odkaz:
http://arxiv.org/abs/2409.14755
Autor:
Puliti, Stefano, Lines, Emily R., Müllerová, Jana, Frey, Julian, Schindler, Zoe, Straker, Adrian, Allen, Matthew J., Winiwarter, Lukas, Rehush, Nataliia, Hristova, Hristina, Murray, Brent, Calders, Kim, Terryn, Louise, Coops, Nicholas, Höfle, Bernhard, Junttila, Samuli, Krůček, Martin, Krok, Grzegorz, Král, Kamil, Levick, Shaun R., Luck, Linda, Missarov, Azim, Mokroš, Martin, Owen, Harry J. F., Stereńczak, Krzysztof, Pitkänen, Timo P., Puletti, Nicola, Saarinen, Ninni, Hopkinson, Chris, Torresan, Chiara, Tomelleri, Enrico, Weiser, Hannah, Astrup, Rasmus
Proximally-sensed laser scanning offers significant potential for automated forest data capture, but challenges remain in automatically identifying tree species without additional ground data. Deep learning (DL) shows promise for automation, yet prog
Externí odkaz:
http://arxiv.org/abs/2408.06507
This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability
Externí odkaz:
http://arxiv.org/abs/2401.15739
Autor:
Xiang, Binbin, Wielgosz, Maciej, Kontogianni, Theodora, Peters, Torben, Puliti, Stefano, Astrup, Rasmus, Schindler, Konrad
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest
Externí odkaz:
http://arxiv.org/abs/2312.15084
Autor:
Puliti, Stefano, Pearse, Grant, Surový, Peter, Wallace, Luke, Hollaus, Markus, Wielgosz, Maciej, Astrup, Rasmus
The FOR-instance dataset (available at https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite
Externí odkaz:
http://arxiv.org/abs/2309.01279
Publikováno v:
Silva Fennica, Vol 52, Iss 1 (2018)
Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy
Externí odkaz:
https://doaj.org/article/dc930ee90a364d86be6e5a5324ecbec6
Autor:
Xiang, Binbin, Peters, Torben, Kontogianni, Theodora, Vetterli, Frawa, Puliti, Stefano, Astrup, Rasmus, Schindler, Konrad
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. It has many obvious applications for outdoor scene underst
Externí odkaz:
http://arxiv.org/abs/2307.02877
This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular appro
Externí odkaz:
http://arxiv.org/abs/2305.02651
Autor:
Lines, Emily R., Allen, Matt, Cabo, Carlos, Calders, Kim, Debus, Amandine, Grieve, Stuart W. D., Miltiadou, Milto, Noach, Adam, Owen, Harry J. F., Puliti, Stefano
With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properti
Externí odkaz:
http://arxiv.org/abs/2212.09937
Autor:
Becker, Alexander, Russo, Stefania, Puliti, Stefano, Lang, Nico, Schindler, Konrad, Wegner, Jan Dirk
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 195, January 2023, Pages 269-286
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data
Externí odkaz:
http://arxiv.org/abs/2111.13154