A COMPARISON OF TREE SEGMENTATION METHODS USING VERY HIGH
DENSITY AIRBORNE LASER SCANNER DATA

Pirotti, F.; Kobal, M.; Roussel, J. R.

doi:https://doi.org/10.5194/isprs-archives-XLII-2-W7-285-2017

DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS

E. Kutchartt, J. Hernández, P. Corvalán, Á. Promis, and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 913–920, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-913-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-913-2022, 2022

SEAWEED PRESENCE DETECTION USING MACHINE LEARNING AND REMOTE SENSING

F. Tonion and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1011–1017, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1011-2022, 2022

ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

E. Kutchartt, M. Pedron, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 431–438, https://doi.org/10.5194/isprs-annals-V-3-2022-431-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-431-2022, 2022

ASSESSMENT OF VOLUME AND ABOVE-GROUND BIOMASS IN ARAUCARIA FOREST THROUGH SATELLITE IMAGES, COMPARING DIFFERENT METHODS IN THE SOUTH OF CHILE

F. Pirotti, E. Kutchartt, and E. Csaplovics

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 331–336, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-331-2020,https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-331-2020, 2020

TRAINING IN INNOVATIVE TECHNOLOGIES FOR CLOSE-RANGE SENSING IN ALPINE TERRAIN – 3RD EDITION

M. Rutzinger, K. Anders, M. Bremer, B. Höfle, R. Lindenbergh, S. Oude Elberink, F. Pirotti, M. Scaioni, and T. Zieher

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 243–250, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020,https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-243-2020, 2020

APPLICATION OF TREE DETECTION METHODS OVER LIDAR DATA FOR FOREST VOLUME ESTIMATION

F. Pirotti, C. Paterno, and M. Pividori

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1055–1060, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1055-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1055-2020, 2020

MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

H. Bernsteiner, N. Brožová, I. Eischeid, A. Hamer, S. Haselberger, M. Huber, A. Kollert, T. M. Vandyk, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 431–437, https://doi.org/10.5194/isprs-annals-V-3-2020-431-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-431-2020, 2020

A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

F. Tonion, F. Pirotti, G. Faina, and D. Paltrinieri

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 565–570, https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020,https://doi.org/10.5194/isprs-annals-V-3-2020-565-2020, 2020

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Giovanni Forzieri, Matteo Pecchi, Marco Girardello, Achille Mauri, Marcus Klaus, Christo Nikolov, Marius Rüetschi, Barry Gardiner, Julián Tomaštík, David Small, Constantin Nistor, Donatas Jonikavicius, Jonathan Spinoni, Luc Feyen, Francesca Giannetti, Rinaldo Comino, Alessandro Wolynski, Francesco Pirotti, Fabio Maistrelli, Ionut Savulescu, Stéphanie Wurpillot-Lucas, Stefan Karlsson, Karolina Zieba-Kulawik, Paulina Strejczek-Jazwinska, Martin Mokroš, Stefan Franz, Lukas Krejci, Ionel Haidu, Mats Nilsson, Piotr Wezyk, Filippo Catani, Yi-Ying Chen, Sebastiaan Luyssaert, Gherardo Chirici, Alessandro Cescatti, and Pieter S. A. Beck

Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020,https://doi.org/10.5194/essd-12-257-2020, 2020

Short summary

Rockfall modelling in forested areas: the role of digital terrain model spatial resolution

Barbara Žabota, Matjaž Mikoš, and Milan Kobal

Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2019-372,https://doi.org/10.5194/nhess-2019-372, 2019

Preprint withdrawn

Short summary

URBAN GEO BIG DATA

M. A. Brovelli, P. Boccardo, G. Bordogna, A. Pepe, M. Crespi, M. Munafò, and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W14, 23–30, https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019,https://doi.org/10.5194/isprs-archives-XLII-4-W14-23-2019, 2019

CLASSIFICATION OF AERIAL LASER SCANNING POINT CLOUDS USING MACHINE LEARNING: A COMPARISON BETWEEN RANDOM FOREST AND TENSORFLOW

F. Pirotti and F. Tonion

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1105–1111, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1105-2019, 2019

DSM AND DTM FOR EXTRACTING 3D BUILDING MODELS: ADVANTAGES AND LIMITATIONS

E. Fissore and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1539–1544, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1539-2019, 2019

DETECTION OF BUILDING ROOFS AND FACADES FROM AERIAL LASER SCANNING DATA USING DEEP LEARNING

F. Pirotti, C. Zanchetta, M. Previtali, and S. Della Torre

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 975–980, https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W11-975-2019, 2019

EXTRACTION AND VISUALIZATION OF 3D BUILDING MODELS IN URBAN AREAS FOR FLOOD SIMULATION

C. E. Kilsedar, F. Fissore, F. Pirotti, and M. A. Brovelli

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W11, 669–673, https://doi.org/10.5194/isprs-archives-XLII-2-W11-669-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W11-669-2019, 2019

PLANNING HARVESTING OPERATIONS IN FOREST ENVIRONMENT: REMOTE SENSING FOR DECISION SUPPORT

M. Piragnolo, S. Grigolato, and F. Pirotti

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W1, 33–40, https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019,https://doi.org/10.5194/isprs-annals-IV-3-W1-33-2019, 2019

INITIAL EVALUATION OF 3D RECONSTRUCTION OF CLOSE OBJECTS WITH SMARTPHONE STEREO VISION

A. Masiero, F. Fissore, M. Piragnolo, A. Guarnieri, F. Pirotti, and A. Vettore

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 289–293, https://doi.org/10.5194/isprs-archives-XLII-1-289-2018,https://doi.org/10.5194/isprs-archives-XLII-1-289-2018, 2018

MIGRATION OF DIGITAL CARTOGRAPHY TO CITYGML; A WEB-BASED TOOL FOR SUPPORTING SIMPLE ETL PROCEDURES

F. Fissore and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 193–200, https://doi.org/10.5194/isprs-archives-XLII-4-193-2018,https://doi.org/10.5194/isprs-archives-XLII-4-193-2018, 2018

COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

M. Piragnolo, G. Lusiani, and F. Pirotti

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1381–1387, https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018,https://doi.org/10.5194/isprs-archives-XLII-3-1381-2018, 2018

OPEN SOURCE WEB TOOL FOR TRACKING IN A LOWCOST MOBILE MAPPING SYSTEM

F. Fissore, F. Pirotti, and A. Vettore

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W8, 99–104, https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W8-99-2017, 2017

BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE

F. Pirotti, F. Sunar, and M. Piragnolo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 335–340, https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-335-2016, 2016

KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

F. Pirotti, D. Travaglini, F. Giannetti, E. Kutchartt, F. Bottalico, and G. Chirici

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 17–22, https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016,https://doi.org/10.5194/isprs-archives-XLI-B7-17-2016, 2016

A COMPARISON OF TREE SEGMENTATION METHODS USING VERY HIGH DENSITY AIRBORNE LASER SCANNER DATA

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