Stefano Puliti

2.7k total citations · 2 hit papers
46 papers, 1.9k citations indexed

About

Stefano Puliti is a scholar working on Environmental Engineering, Insect Science and Nature and Landscape Conservation. According to data from OpenAlex, Stefano Puliti has authored 46 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Environmental Engineering, 22 papers in Insect Science and 19 papers in Nature and Landscape Conservation. Recurrent topics in Stefano Puliti's work include Remote Sensing and LiDAR Applications (41 papers), Forest Ecology and Biodiversity Studies (22 papers) and Forest ecology and management (19 papers). Stefano Puliti is often cited by papers focused on Remote Sensing and LiDAR Applications (41 papers), Forest Ecology and Biodiversity Studies (22 papers) and Forest ecology and management (19 papers). Stefano Puliti collaborates with scholars based in Norway, Switzerland and Italy. Stefano Puliti's co-authors include Erik Næsset, Terje Gobakken, Hans Ole Ørka, Rasmus Astrup, Carlos Çabo, James E. O’Connor, J. Rosette, Livia Piermattei, Johannes Breidenbach and Svein Solberg and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and Sensors.

In The Last Decade

Stefano Puliti

41 papers receiving 1.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Structure from Motion Photogrammetry in Forestry: a Review

2019 416 citations Carlos Çabo, Stefano Puliti et al. Current Forestry Reports profile →
Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning

2024 51 citations Maciej Wielgosz, Stefano Puliti et al. Remote Sensing of Environment profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Stefano Puliti Norway	22	1.6k	873	579	567	479	46	1.9k
Peter Surový Czechia	22	1.4k 0.9×	902 1.0×	621 1.1×	614 1.1×	478 1.0×	105	2.1k
Peter Krzystek Germany	23	1.8k 1.1×	860 1.0×	413 0.7×	879 1.6×	733 1.5×	53	2.1k
Eetu Puttonen Finland	27	1.4k 0.9×	813 0.9×	437 0.8×	517 0.9×	240 0.5×	80	2.0k
Sakari Tuominen Finland	25	1.6k 1.0×	1.1k 1.2×	306 0.5×	817 1.4×	424 0.9×	71	2.1k
Markus Hollaus Austria	31	2.3k 1.4×	1.1k 1.3×	655 1.1×	1.1k 1.9×	739 1.5×	106	2.8k
Markus Holopainen Finland	20	1.4k 0.9×	631 0.7×	275 0.5×	877 1.5×	538 1.1×	47	1.6k
Jonathan P. Dandois United States	10	1.1k 0.7×	764 0.9×	507 0.9×	246 0.4×	230 0.5×	12	1.4k
Midhun Mohan United States	21	1.1k 0.7×	616 0.7×	219 0.4×	524 0.9×	351 0.7×	68	1.6k
Paula Litkey Finland	26	2.1k 1.3×	1.2k 1.4×	949 1.6×	662 1.2×	457 1.0×	48	2.7k
J. Rosette United Kingdom	17	1.1k 0.7×	871 1.0×	266 0.5×	555 1.0×	211 0.4×	54	1.6k

Countries citing papers authored by Stefano Puliti

Since Specialization

Citations

This map shows the geographic impact of Stefano Puliti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stefano Puliti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefano Puliti more than expected).

Fields of papers citing papers by Stefano Puliti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stefano Puliti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stefano Puliti. The network helps show where Stefano Puliti may publish in the future.

Co-authorship network of co-authors of Stefano Puliti

This figure shows the co-authorship network connecting the top 25 collaborators of Stefano Puliti. A scholar is included among the top collaborators of Stefano Puliti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Stefano Puliti. Stefano Puliti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Blattert, Clemens, et al.. (2025). Designing the Forest of Tomorrow: Generating Virtual Trees with Adversarial Autoencoders. The international archives of the photogrammetry, remote sensing and spatial information sciences. XLVIII-G-2025. 619–624.

Puliti, Stefano, et al.. (2025). Towards enhancing field‐based vegetation monitoring: A deep learning approach for species coverage estimation from ground‐level imagery. Methods in Ecology and Evolution. 16(5). 949–957.

Hoseini, Mostafa, et al.. (2024). RoadSens: An integrated near-field sensor solution for 3D forest road monitoring. Computers and Electronics in Agriculture. 229. 109710–109710.

Hoseini, Mostafa, Stefano Puliti, Stephan Hoffmann, & Rasmus Astrup. (2023). Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams. International Journal of Forest Engineering. 35(2). 303–312. 5 indexed citations

Wielgosz, Maciej, Stefano Puliti, Phil Wilkes, & Rasmus Astrup. (2023). Point2Tree(P2T)—Framework for Parameter Tuning of Semantic and Instance Segmentation Used with Mobile Laser Scanning Data in Coniferous Forest. Remote Sensing. 15(15). 3737–3737. 21 indexed citations

Puliti, Stefano, et al.. (2023). TOWARDS ACCURATE INSTANCE SEGMENTATION IN LARGE-SCALE LIDAR POINT CLOUDS. SHILAP Revista de lepidopterología. X-1/W1-2023. 605–612. 10 indexed citations

Dalponte, Michele, Hans Ole Ørka, Erik Næsset, et al.. (2022). UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce. Remote Sensing. 14(15). 3830–3830. 8 indexed citations

Russo, Stefania, et al.. (2022). Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles. ISPRS Journal of Photogrammetry and Remote Sensing. 195. 269–286. 35 indexed citations

Lines, Emily R., Matthew R. Allen, Carlos Çabo, et al.. (2022). AI applications in forest monitoring need remote sensing benchmark datasets. 2022 IEEE International Conference on Big Data (Big Data). 4528–4533. 18 indexed citations

10.

Rizzoli, Paola, et al.. (2022). Large Scale Forest Parameter Estimation Through a Deep Learning-Based Fusion of Sentinel-2 and Tandem-X Data. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 5773–5776. 1 indexed citations

11.

Puliti, Stefano & Rasmus Astrup. (2022). Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery. International Journal of Applied Earth Observation and Geoinformation. 112. 102946–102946. 37 indexed citations

12.

Giannetti, Francesca, Nicola Puletti, Stefano Puliti, Davide Travaglini, & Gherardo Chirici. (2020). Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests. Ecological Indicators. 117. 106513–106513. 22 indexed citations

13.

Steyaert, Sam M. J. G., et al.. (2019). Reindeer carcasses provide foraging habitat for insectivorous birds of the alpine tundra. 42. 36–40. 5 indexed citations

14.

Çabo, Carlos, et al.. (2019). Structure from Motion Photogrammetry in Forestry: a Review. Current Forestry Reports. 5(3). 155–168. 416 indexed citations breakdown →

15.

Giannetti, Francesca, Gherardo Chirici, Terje Gobakken, et al.. (2018). A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data. Remote Sensing of Environment. 213. 195–205. 88 indexed citations

16.

Puliti, Stefano, Bruce Talbot, & Rasmus Astrup. (2018). Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery. Forests. 9(3). 102–102. 52 indexed citations

17.

Puliti, Stefano, Svetlana Saarela, Terje Gobakken, Göran Ståhl, & Erik Næsset. (2017). Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference. Remote Sensing of Environment. 204. 485–497. 134 indexed citations

18.

Puliti, Stefano, Svein Solberg, Erik Næsset, et al.. (2017). Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data. Remote Sensing. 9(10). 984–984. 11 indexed citations

19.

Puliti, Stefano, Liviu Theodor Ene, Terje Gobakken, & Erik Næsset. (2017). Use of partial-coverage UAV data in sampling for large scale forest inventories. Remote Sensing of Environment. 194. 115–126. 92 indexed citations

20.

Puliti, Stefano, Hans Ole Ørka, Terje Gobakken, & Erik Næsset. (2015). Inventory of Small Forest Areas Using an Unmanned Aerial System. Remote Sensing. 7(8). 9632–9654. 292 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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