VIRGINIA COLETTA

Dottoressa di ricerca

ciclo: XXXV


supervisore: Prof. Mattia Crespi
relatore: Dr. Ing. Paolo Allasia (CNR-IRPI - Torino), Dr. Alessandra Bonazza (CNR-ISAC - Bologna), Dr. Ing. Stefano Dietrich (CNR-ISAC - Roma)

Titolo della tesi: Automated 3D change detection for land use evaluation using data science techniques

An increase in urbanization resulting from an increase in economic activities within urban contexts has characterized the last decades. This phenomenon has caused an intensification of overbuilding that must be monitored constantly. One of the most widely used methodologies to carry out this type of monitoring is the analysis of remotely sensed data (RS), as it can provide useful and consistent information on urban morphological structures with different spatial and temporal resolutions, allowing for long-term spatiotemporal analyses of the historical development of cities and thus monitoring the evolution of their urbanisation patterns, an objective closely related to the United Nations Sustainable Development Goals (SDGs) (SDG 11 - Sustainable Cities and Communities). In this context, most of the recently developed Change Detection (CD) methodologies rely on deep learning architectures. Nevertheless, this kind of algorithm mainly focuses on the generation of two-dimensional (2D) change maps, in which the planimetric (2D) extent of the areas affected by changes is identified without providing any information on the corresponding elevation (3D) changes. The aim of this work is therefore to lay the foundations for the development of DL algorithms capable of automatically generating a (3D) elevation CD map together with a standard 2D CD map, using only bitemporal optical images as input and thus without the need to rely directly on elevation data during the inference phase. In particular, our work proposes two innovative networks capable of solving the 2D and 3D CD tasks simultaneously, and a new and freely available dataset, named 3DCD dataset designed precisely for this multitasking. Specifically, the dataset covers the urban area of Valladolid and is composed of: - 472 pairs of images cropped from optical orthophotos acquired through two different aerial surveys (performed respectively in 2010 and 2017); - 472 pairs of DSMs produced from the rasterization of the point clouds acquired through two different LiDAR flights covering the same area and years (2010 and 2017) of the orthophotos; - the corresponding 472 2D CD maps in raster format; - the corresponding 472 3D CD maps -- the elevation variation maps -- in raster format. The proposed architectures consist of a deep convolutional neural network (CNN) (SUNet) and a transformer-based network (MTBIT). Encouraging results are shown on the 3DCD dataset by comparing the proposed architectures with other networks specifically designed to solve the 2D CD task and modified by us to also solve the 3D CD task. In particular, MTBIT achieves a normalized root-mean-square error (nRMSE) of 6.33 m, a change root-mean-square error (cRMSE) of 5.60 m and an F1 score of 62.80 \% - the best performance among the compared architectures - with a limited number of parameters (13.1 M). Conversely, SUNet achieves an nRMSE of 6.48 m, a cRMSE of 5.72 m and an F1 score of 58.80 \% but with a significantly higher number of parameters (35.7 M). The results are promising considering that the proposed network architectures were trained on a few images. Finally, we implemented innovative data augmentation techniques to improve the training phase and thus performance. We only used MTBIT, the network that gave the best results. We developed different data augmentation techniques according to the specific task the network has to perform, and the results obtained are quite encouraging. The quantitative evaluation shows that each new technique has a positive impact on reducing nRMSE and cRMSE values. The most efficient method is the combination of individual strategies. In particular, the best is the Crop or Resize strategy coupled with Gaussian noise on the 3D map, which achieves nRMSE values of 5.88 m, cRMSE values of 5.34 m and an F1 score of 65.16%. The 3DCD code and dataset are available at https://sites.google.com/uniroma1.it/3dchangedetection/home-page.

Produzione scientifica

11573/1725698 - 2024 - Urban 3D Change Detection with Deep Learning: Custom Data Augmentation Techniques
Contu, Riccardo; Marsocci, Valerio; Coletta, Virginia; Ravanelli, Roberta; Scardapane, Simone - 04d Abstract in atti di convegno
congresso: EGU General Assembly 2024 (Vienna, Austria)
libro: EGU General Assembly 2024 - ()

11573/1665436 - 2023 - Inferring 3D change detection from bitemporal optical images
Marsocci, Valerio; Coletta, Virginia; Ravanelli, Roberta; Scardapane, Simone; Crespi, Mattia - 01a Articolo in rivista
rivista: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (Amsterdam Netherlands: Elsevier BV) pp. 325-339 - issn: 0924-2716 - wos: WOS:000923862700001 (10) - scopus: 2-s2.0-85146148106 (14)

11573/1656314 - 2022 - 3DCD: a new dataset for 2d and 3d change detection using deep learning techniques
Coletta, V.; Marsocci, V.; Ravanelli, R. - 04c Atto di convegno in rivista
rivista: INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES (ISPRS Council) pp. 1349-1354 - issn: 1682-1750 - wos: WOS:000855647800190 (7) - scopus: 2-s2.0-85131926156 (8)
congresso: 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences) (Nice, France)

11573/1617022 - 2021 - Multi-instrumental Analysis of the Extreme Meteorological Event Occurred in Matera (Italy) on November 2019
Coletta, V.; Mascitelli, A.; Bonazza, A.; Ciarravano, A.; Federico, S.; Prestileo, F.; Torcasio, R. C.; Dietrich, S. - 04b Atto di convegno in volume
congresso: 21st International Conference on Computational Science and Its Applications, ICCSA 2021 (Cagliari; Italy)
libro: Computational Science and Its Applications – ICCSA 2021 : 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII - (978-3-030-87009-6; 978-3-030-87010-2)

11573/1489589 - 2020 - Pyrgi. Analysis of possible climatic effects on a coastal archaeological site
Coletta, Virginia; Allasia, Paolo; Bonazza, Alessandra; Ciarravano, Alessandro; Federico, Stefano; Notti, Davide; Prestileo, Fernanda; Claudia Torcasio, Rosa; Crespi, Mattia Giovanni; Dietrich, Stefano - 02a Capitolo o Articolo
libro: Eighth International Symposium “Monitoring of Mediterranean Coastal Areas. Problems and Measurement Techniques” - ()

11573/1336134 - 2019 - Monitoraggio Della Stabilità Meccanica Degli Individui Arborei Tramite Un Sensore Gnss A Basso Costo
Coletta, Virginia; Mascitelli, Alessandra; Pierluigi, Bombi; Bruno De Cinti, ; Stefano, Federico; Giorgio, Matteucci; Mazzoni, Augusto; Muzzini, Valerio G.; Igor, Petenko; Stefano, Dietrich - 04b Atto di convegno in volume
congresso: 22 Convegno Nazionale di Agrometeorologia Ricerca ed innovazione per la gestione del rischio meteo-climatico in agricoltura (Napoli; Italia)
libro: Atti del XXII Convegno Nazionale di Agrometeorologia. Ricerca ed innovazione per la gestione del rischio meteo-climatico in agricoltura - (978-88-5497-000-7)

11573/1445732 - 2019 - Fulmini ed agricoltura in tempi di cambiamento climatico
Dietrich, Stefano; Coletta, Virginia; D’Adderio, Leo Pio; Federico, Stefano; Pazienza, Luigi; Torcasio, Rosa Claudia - 04b Atto di convegno in volume
congresso: 22 Convegno Nazionale di Agrometeorologia Ricerca ed innovazione per la gestione del rischio meteo-climatico in agricoltura (Napoli, Italia)
libro: Atti del XXII Convegno Nazionale di Agrometeorologia. Ricerca ed innovazione per la gestione del rischio meteo-climatico in agricoltura - (978-88-5497-000-7)

11573/1358043 - 2019 - Tree Motion. Following the wind-induced swaying of arboreous individual using a GNSS receiver
Mascitelli, Alessandra; Bombi, Pierluigi; Coletta, Virginia; De Cinti, Bruno; Federico, Stefano; Matteucci, Giorgio; Mazzoni, Augusto; Muzzini, Valerio G.; Petenko, Igor; Dietrich, Stefano - 01a Articolo in rivista
rivista: ITALIAN JOURNAL OF AGROMETEOROLOGY (Granarolo dell'Emilia, Bologna : Patron) pp. 25-36 - issn: 2038-5625 - wos: WOS:000504830700003 (7) - scopus: 2-s2.0-85078629102 (8)

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