Wielgosz, M., Puliti. S., Xiang, B., Schindler, K., Astrup, R. (2024). SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sensing of Environment, 313, https://doi.org/10.1016/j.rse.2024.114367.

Nahorna, O.,  Noordermeer, L., Gobakken, T., Eyvindson, K. (2024). Assessing the importance of detailed forest inventory information using stochastic programming. Canadian Journal of Forest Research, https://doi.org/10.1139/cjfr-2023-0218.

Bielza, J.C., Noordermeer, L., Næsset, E., Gobakken, T., Breidenbach, J., Ørka, H.O. (2024). Predicting tree species composition using airborne laser scanning and multispectral data in boreal forests. Science of Remote Sensing, 100154. https://doi.org/10.1016/j.srs.2024.100154.

Sandvik, Y.J., Futsæther, C.M., Liland, K.H., Tomic, O. (2024). A Comparative Literature Review of Machine Learning and Image Processing Techniques Used for Scaling and Grading of Wood Logs. Forests, 15:7, 1243, https://doi.org/10.3390/f15071243.

Cattaneo, N., Puliti, S., Fischer, C., Astrup, R. (2024). Estimating wood quality attributes from dense airborne LiDAR point clouds. Forest Ecosystems, 11, 100184, https://doi.org/10.1016/j.fecs.2024.100184.

Xiang, B., Wielgosz, M., Kontogianni, T., Peters, T., Puliti, S., Astrup, R., Schindler, K. (2024).  Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning. Remote Sensing of Environment, 305, 114078, https://doi.org/10.1016/j.rse.2024.114078.

Ståhl, G., Gobakken, T., Saarela, S., Persson, H.J., Ekström, M., Healey, S.P., Yang, Z., Holmgren, J., Lindberg, E., Nyström, K., Papucci, E., Ulvdal, P., Ørka, H.O., Næsset, E., Hou, Z., Olsson, H., Roberts, R.E. (2024). Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications. Forest Ecosystems, 11, 100164, DOI: 10.1016/j.fecs.2023.100164.

Hoseini, M., Puliti, S., Hoffmann, S.,  Astrup, R. (2023). Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams. International Journal of Forest Engineering, DOI: 10.1080/14942119.2023.2290795.

Noordermeer, L., Ørka, H.O., Gobakken, T. (2023). Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches. Silva Fennica, 57:3, article id 23023, https://doi.org/10.14214/sf.23023.

Noordermeer, L., Korpunen, H., Berg, S., Gobakken, T., Astrup, R. (2023). Economic losses caused by butt rot in Norway spruce trees in Norway. Scandinavian Journal of Forest Research, 38:7-8, 497-505, DOI: 10.1080/02827581.2023.2273252.

Straker, A., Puliti, S., Breidenbach, J., Kleinn, C., Pearse, G., Astrup, R., Magdon, P. (2023). Instance segmentation of individual tree crowns with YOLOv5: A comparison of approaches using the ForInstance benchmark LiDAR dataset. ISPRS Open Journal of Photogrammetry and Remote Sensing, https://doi.org/10.1016/j.ophoto.2023.100045.

Hansen, E., Rahlf, J., Astrup, R., Gobakken, T. (2023). Taper, volume, and bark thickness models for spruce, pine, and birch in Norway. Scandinavian Journal of Forest Research, 38:6, 413-428,  https://doi.org/10.1080/02827581.2023.2243821.

Wielgosz, M., Puliti, S., Wilkes, P., Astrup, R. (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, https://doi.org/10.3390/rs15153737.

Moan, M.Å., Noordermeer, L., White, J.C.,  Coops, N.C., Bollandsås, O.M. (2023). Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data. Forestry: An International Journal of Forest Research, 2023; cpad025, https://doi.org/10.1093/forestry/cpad025.

Noordermeer, L., Næsset, E., Gobakken, T. (2022). Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests. Silva Fennica, 56(1). https://doi.org/10.14214/sf.10608.

Puliti, S., McLean, J.P., Cattaneo, N., Fischer, C., Astrup, R. (2022). Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning. Forestry: An International Journal of Forest Research, https://doi.org/10.1093/forestry/cpac026.

Puliti, S., Astrup, R. (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. https://doi.org/10.1016/j.jag.2022.102946.

Lingren, N., Nyström, K., Saarela, S., Olsson H., Ståhl, G. (2022). Importance of calibration for improving the efficiency of data assimilation for predicting forest characteristics. Remote Sensing, 14(18), 4627. https://doi.org/10.3390/rs14184627.

Noordermeer, L., Sørngård, E., Astrup, R., Næsset, E., Gobakken, T. (2021). Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees. International Journal of Forest Engineering, 32(2), 119-127. https://doi.org/10.1080/14942119.2021.1899686.

Saarela, S., Gobakken, T., Ørka, H.O., Bollandsås, O.M., Næsset, E., Ståhl, G. (2024). Handling Single-Year Big Data in Forest Inventory Systems Based on Remote Sensing and Multi-Temporal Data Assimilation. Available at SSRN: https://ssrn.com/abstract=4838693 or http://dx.doi.org/10.2139/ssrn.4838693

Puliti, S., Fischer, C., Astrup, R. (2024). BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach. https://doi.org/10.48550/arXiv.2409.14755

Puliti, S., Lines, E. R., Müllerová, J., Frey, J., Schindler, Z., Straker, A., Allen, M.J., Winiwarter, L., Rehush, N., Hristova, H., Murray, B., Calders, K., Terryn, L., Coops, N., Höfle, B., Junttila, S., Krůček, M., Krok, G., Král, K., Levick, S.R., Luck, L., Missarov, A., Mokroš, M., Owen, H.J.F., Stereńczak, K., Pitkänen, T.P., Puletti, N., Saarinen, N., Hopkinson, C., Torresan, C., Tomelleri, E., Weiser, H., Astrup, A. (2024). Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset. https://doi.org/10.48550/arXiv.2408.06507.

Xiang, B., Peters, T., Kontogianni, T., Vetterli, F., Puliti, S., Astrup, R., Schindler. K. (2023). Towards accurate instance segmentation in large scale lidar point clouds. https://arxiv.org/pdf/2307.02877.

Hoseini, M., Gobakken H.R., Hoffmann S., Horvart, C., Rahlf, J., Bjerketvedt, j., Puliti, S., Astrup, R. (2024) Roadsens: An Integrated Near-Field Sensor Solution for 3d Forest Road Monitoring. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4895956

Astrup, R. (2024). AI i skovbruget – igangværende projekter og perspektiver på sigt. Skovbrugskonferencen 2024. Bredsten, Danmark, October 3, 2024.

Puliti, S., Wielgosz, M., Xiang, B., Schindler, K., Astrup, R. (2024). SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. ForestSat2014, Rotorua, New Zealand, September 09-13, 2024.

Cattaneo, N., Puliti, S., Fischer, C., Astrup, R. (2024). Estimating wood quality attributes from dense airborne lidar point clouds. 26th IUFRO World Congress 2024, Stockholm, Sweden, June 23-29, 2024.

Saarela, S., Gobakken, T., Ørka, H.O., Bollandsås, O.M., Næsset, E., Ståhl, G. (2024). Data assimilation – a tool for making use of big data to improve forest stand information. 26th IUFRO World Congress 2024, Stockholm, Sweden, June 23-29, 2024.

Nahorna, O., Moan, M., Noordermeer, L., Eyvindson, K. (2024). Justifications for improving site productivity evaluations in forest planning: a Value of Information approach. 26th IUFRO World Congress 2024, Stockholm, Sweden, June 23-29, 2024.

Puliti, S., Wielgosz, M., Rahlf, J., Astrup, R. (2024). Four years of deep learning for forest monitoring and management in a nutshell. 26th IUFRO World Congress 2024, Stockholm, Sweden, June 23-29, 2024.

Hoffmann, S., Hoseini, M., Schönauer, M., Astrup, R. (2024). Forwardsens: A Conceptual Flexible Sensor Solution Integrated Into Forestsens To Track Forwarder Loads Independent of Equipment Manufacturer’s On-Board Systems and Fleet Management Solutions. 56^th International Symposium on Forestry Mechanization (FORMEC), Gdańsk, Poland, June 11-14, 2024.

Hoseini, M., Gobakken, H.R., Hoffmann, S., Horvath, C., Rahlf, J., Bjerketvedt, J., Puliti, S., Astrup, R. (2024). A Sensor Solution For Automated Measurements of Forest Roads. 56^th International Symposium on Forestry Mechanization (FORMEC), Gdańsk, Poland, June 11-14, 2024.

Fischer, C., Hoseini, M. (2024). Can Log Geometry Be Used For Traceability Within The Forest Value Chain? (Poster). 56^th International Symposium on Forestry Mechanization (FORMEC), Gdańsk, Poland, June 11-14, 2024.

Gobakken, H.R., Hoseini, M., Hoffmann, S., Bjerketvedt, J., Rahlf, J., Astrup, R. (2024). Classifying Forest Roads Using Geometric Features Detected With The Mobile Proximal Sensing Platform Roadsens. 56^th International Symposium on Forestry Mechanization (FORMEC), Gdańsk, Poland, June 11-14, 2024.

Rahlf, J. (2024). Enhancing Digital Forestry through AI and IoT Integration in OCI with ForestSens. OUGN 2024, Oslo, Norway, April 24-25, 2024.

Fischer, C. (2024) Merking av tømmer – fra stubbe til industri. Tømmer og Høggere 2024, Sundvollen Hall, Norway. April 10, 2024.

Astrup, R. (2024) SmartForest and AI: and overview. SmartForest Open seminar on Deep Learning and AI in Forestry. March 21, 2024.

Puliti, S. (2024) 4 years of developmentin forest point cloud deep learning. SmartForest Open seminar on Deep Learning and AI in Forestry. March 21, 2024.

Wielgosz, M. (2024) Architecture and technical challenges for 3 generations of point cloud segmentation systems. SmartForest Open seminar on Deep Learning and AI in Forestry. March 21, 2024.

Astrup, R. (2024) ForestSens – making the algorithms available. SmartForest Open seminar on Deep Learning and AI in Forestry. March 21, 2024.

Rahlf, J. (2024). ForestSens: Revolusjonerer Skogbruket med Oracle Data Sciences og APEX. Oracle Cloud Summit 2024, Oslo, Norway, February 14, 2024.

Astrup, R. (2023). SmartForest. Annual Mistra Digital Forest meeting. Stockholm, Sweden, November 25, 2023. 

Fischer, C., Hoseini, M., Sandvik, Y.J., Horvath, C., Astrup, R. (2023). Enhancing efficiency and value through full traceability of timber from the forest to the sawmill. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Hoseini, M., Fischer, F., Wielgosz, M., Horvath, C., Astrup, R. (2023). Poster: Assessing the outer shape of sawlogs at the mill gate using stereo cameras and deep learning. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Horvath, C., Hanssen, K.H., Berg, S., Astrup, R. (2023). SmartPlanter: a planting device for precision planting. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Hoffmann, S., Hoseini, M., Puliti, S., Astrup, R. (2023). Forest road surface monitoring using GNSS-aided dashcams and computer vision. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Rahlf, J., Puliti, S., Astrup, R. (2023). ForestSens: combining sensors and AI for sustainable forest management and operations. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Rahlf, J., Hoffmann, S., Astrup, R. (2023). Poster: Forest road geometry extraction with AI and large area airborne laser scanning. 55th International Symposium on Forest Mechanization (FORMEC) and the 7th Forest Engineering Conference (FEC), Florence, Italy, September 20-24, 2023.

Puliti, S. (2023). Keynote: Open data and AI translating the language of trees. SilviLaser 2023, London, Great Britain, September 06-08, 2023.

Bielza, C.J., Noordermeer, L., Næsset, E., Gobakken, T., Breidenbach, J., Ørka, H.O. (2023). Predicting tree species composition using airborne laser scanning and spectral data. SilviLaser 2023, London, Great Britain, September 06-08, 2023.

Noordermeer, L., Ørka,. H.O., Gobakken, T. (2023). Poster: Imputing stem frequency distributions using harvester and airborne laser scanner data. SilviLaser 2023, London, Great Britain, September 06-08, 2023.

Moan, M.Å., Noordermeer, L., Bollandsås, O.M. (2023). Poster: Site index determination using a time series of airborne laser scanning data. SilviLaser 2023, London, Great Britain, September 06-08, 2023.

Nahorna, O., Gobakken, T., Noordermeer, L., Eyvindson, K. (2023). Quantifying the value of using detailed forest inventory information in a Norwegian context. IBFRA Conference, Helsinki, Finland, August 28-31, 2023.

Saarela, S., Gobakken, T., Ørka, H.O., Bollandsås, O.M., Næsset, E., Ståhl, G. (2023). Data assimilation for forest inventory: first Norwegian experiences. IBFRA Conference, Helsinki, Finland, August 28-31, 2023.

Astrup, R. (2023). SmartForest. Advancing Silvicultural Technology, Umeå, Sweden, August 22-24, 2023.

Horvath, C., Hanssen, K.H., Berg, S., Astrup, R. (2023). A cloud-connected planting: SmartPlanter for precision planting. Advancing Silvicultural Technology. Umeå, Sweden, August 22-24, 2023.

Hanssen, K.H., Berg, S., Horvath, C. (2023). Poster: Time consumption of high accuracy planting. Advancing Silvicultural Technology. Umeå, Sweden, August 22-24, 2023.

Puliti, S., Hanssen, K.H., Astrup, R. (2023). Use of drones and deep learning in forest regeneration surveys. Advancing Silvicultural Technology. Umeå, Sweden, August 22-24, 2023.

Astrup, R. (2023). Keynote: The digital forest: opportunities for innovation and improved forest management. Growth and Yield Innovations Conference 2023. Canmore, Alberta, Canada, June 18–21, 2023.

Rahlf, J., Puliti, S., Astrup, R. (2023). Empowering Sustainable Forest Management with AI:  The ForestSens Experience. Geospatial World Forum 2023, Rotterdam, The Netherlands, May 02-05, 2023.

Rahlf, J., Astrup, R., Puliti, S. (2023). ForestSens – Empowering the digital forest value chain. OUGN2023 – Spring Seminar for Oracle users, Oslo, Norway, April, 24-25, 2023.

Astrup, R. (2024) Towards improved characterization of forest structure with high resolution 3D point clouds. Forest Ecosystems Spring Workshop 2024. Beijing Forestry University, China, April 23, 2024.

Astrup, R. (2023). Keynote: Supporting sustainable forest management through improved information flow and AI. Artificial Intelligence and Ecosystem Management Conference. Palencia, Spain, April 18-21, 2023.

Gobakken, T., Noordermeer, N. (2023). Råtedata fra hogstmaskin. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Gobakken, T., Rahlf, J., Hansen, E. (2023). Nye volum- og avsmalningsfunksjoner. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Roald B.J. (2023). Bildematching – hva er det og hvordan kan vi bruke det? Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Ørka, H.O., Jutras-Perreault, M.C. (2023). Teknologisk status for fjernmåling av miljøverdier. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Ørka, H.O., Bielza, J.C. (2023). Treslagsinformasjon fra fjernmåling. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Moan, M.Å. (2023). Pågående forsking om bonitering med laser. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Ørka, H.O.(2023). Hvorfor fungerte ikke bildematching i Rendalen? Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Rahlf, J. (2023). Kunstig intelligens: Praktiske resultater med dronedata. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Noordermeer, L., Gobakken, T. (2023). Bruk av hogstmaskindata for skoginventering. Takst- og planseminar 2023, Holmen Fjordhotell, Norway, March 09-10, 2023.

Astrup, R., (2023). SmartForest. Tømmer og Høggere 2024, Sundvollen Hotell, Norway. February 13, 2023.

Rahlf, J., Göhl, M., Puliti, S. (2022). Obtaining forest road geometry features from airborne laser scanning using deep learning. 9th ForestSAT 2022 Conference, Berlin, Germany, 09.08. – 03.09. 2022.

Puliti, S. (2022). Droner i SmartForest. Skog & Tre konferanse, Quality Airport Hotel Gardemoen, Norway, June 2-3, 2022.

Noordermeer, L., Gobakken, T. (2022). Verktøy for optimal aptering. Skog & Tre konferanse, Quality Airport Hotel Gardemoen, Norway, June 2-3, 2022.

Erik Næsset (2021) Fremtidens skogbruksplanlegging. Utvikling av skogbruksplaner: historikk, fra bakken og opp i lufta, laser, SR-16, satellitter – hva videre?. Skogforum Honne 2021, Honne, Norway, November 04-05, 2021

Räty, J., Astrup, R., Breidenbach, J. (2021). Model-Assisted Estimation of Timber Volume by Means of Harvester and ALS Data. SilviLaser 2021, Vienna, Austria, September 28- 30, 2021.

Noordermeer, L., Næsset, E., Gobakken, T. (2021). Estimating Timber Volume using Harvester Data and Airborne Laser Scanner Data from Multiple Acquisitions. SilviLaser 2021, Vienna, Austria, September 28- 30, 2021. https://doi.org/10.34726/wim.1906.

Bjørnbet, J.E. (2024). Identification and characterization of retention trees with airborne laser scanning. NMBU

Sandum, H.N. (2023). Effekten av redusert arealdekning av tilgjengelig beregningsceller på presisjonen til bonitetsestimat ved anvendelse av direkte metode for skogbonitering. NMBU.

Låg, T.T. (2023). Fjernmåling som verktøy i kartlegging av geologiske landformer i skog. NMBU.

Jønland, H.S.G. (2023). Tree species classification with Sentinel-2 time series, laser data and climatic variables through random forest. NMBU.

Botnevik, G. (2022). Assessing the timing of final felling based on saw timber price matrices of Norway spruce (Picea abies) – A case study for Fritzøe Skoger AS.  NMBU.

Råheim, M. (2021). Use of harvester data as referensedata when mapping totalvolume and sawlogvolume. NMBU.