Treffer: Machine learning algorithms of remote sensing data processing for mapping changes in land cover types over Central Apennines, Italy

Title:
Machine learning algorithms of remote sensing data processing for mapping changes in land cover types over Central Apennines, Italy
Authors:
Lemenkova, Polina
Contributors:
Alma Mater Studiorum Università di Bologna = University of Bologna (UNIBO), University of Bologna
Source:
Doctoral. Italy. 2025. :31-31
Publisher Information:
CCSD, 2025.
Publication Year:
2025
Collection:
collection:SDE
collection:GIP-BE
Subject Terms:
GRASS GIS, Cartography, Machine learning, Machine Learning, classification, Image processing, Satellite image, Image classification, Earth Sciences, Environmental sciences, Environment, Sustainable Development, Biodiversity, Land management and planning, [INFO]Computer Science [cs], [INFO.INFO-DS]Computer Science [cs], Data Structures and Algorithms [cs.DS], [SDE]Environmental Sciences, [SDE.BE]Environmental Sciences, Biodiversity and Ecology, [SDU.STU]Sciences of the Universe [physics], [SDV.BID]Life Sciences [q-bio], [SDV.EE]Life Sciences [q-bio], Ecology, environment
Original Identifier:
HAL: hal-05145071
Document Type:
Buch lecture<br />Lectures
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.5281/zenodo.15807478
DOI:
10.5281/zenodo.15807478
Availability:
https://hal.science/hal-05145071
Rights:
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.05145071v1
Database:
HAL

Weitere Informationen

Doctoral
These slides present research on mapping land cover types in Central Apennines, Italy. Monitoring biodiversity of the Mediterranean region is fundamental for land management in protected areas. Mountainous landscapes of Central Italy have large variations of patches and complex mosaic of vegetation. The machine learning (ML) algorithms of remote sensing (RS) data processing were applied to solve the challenges of heterogeneous patterns recognition. The scripting cartographic software GRASS GIS was used for satellite image processing Landsat 8-9 OLI/TIRS. Several algorithms of ML methods were tested, compared and used for image analysis. The results present a time series analysis of several satellite images on period of 2014-2024. The slides were presented at the workshop "A data-driven approach to reconstruct vegetation change in rewilding areas" organised by BIOME – Biodiversity & MacroEcology Laboratory, Dipartimento di Scienze Biologiche, Geologiche ed Ambientali – BiGeA Alma Mater Studiorum - University of Bologna on 11-13 February 2025 in Bologna, Italy.