Results 21 to 30 of about 2,668 (204)
GEOBIA systems for massive data processing [PDF]
GEOBIA 2016: Solutions and synergies, 2016 Large portions of the Earth’s landscape are now captured by high-resolution remotely-sensed datasets and turned into corresponding thematic data. Despite these advancements the number of comprehensive, high-resolution land-cover maps is surprisingly low.
N. Ahles +4 more
openaire +1 more source
Trying to break new ground in aerial archaeology [PDF]
, 2016 Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived ...
Sevara, Christopher, Verhoeven, Geert
core +6 more sources
Geographic Object-Based Image Analysis: A Primer and Future Directions
Remote Sensing, 2020 Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects.
Maja Kucharczyk +3 more
doaj +1 more source
A Framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 GEOBIA (Geographic Object-Based Image Analysis) is not only a hot topic of current remote sensing and geographical research. It is believed to be a paradigm in remote sensing and GIScience.
H. Y. Gu, H. T. Li, L. Yan, X. J. Lu
doaj +1 more source
Mapeamento Geomorfológico Regional Semiautomatizado por Descoberta e Modelagem do Conhecimento
Revista Brasileira de Geomorfologia, 2021 O objetivo do presente estudo é a elaboração de um mapa geomorfológico do estado do Rio de Janeiro na escala 1:250.000, a partir do desenvolvimento de uma metodologia que envolva uma abordagem que minimize a subjetividade inerente à atividade de ...
Raphael e Silva Girão +3 more
doaj +1 more source
Detecting Atlantic forest patches applying GEOBIA and data mining techniques [PDF]
GEOBIA 2016: Solutions and synergies, 2016 Brazilian Atlantic Forest is one of the most devastated tropical forests in the world. Considering that approximately only 12% of its original extent still exists, studies in this area are highly relevant. In this context, this study maps the land cover of Atlantic Forest within the Protected Area of ‘Macae de Cima’, in Rio de Janeiro State, Brazil ...
C D Girolamo Neto +3 more
openaire +1 more source
Leveraging OSM and GEOBIA to Create and Update Forest Type Maps [PDF]
ISPRS International Journal of Geo-Information, 2020 Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest).
Melanie Brauchler, Johannes Stoffels
openaire +3 more sources
Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2
Remote Sensing, 2022 Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some ...
Melanie Brauchler +2 more
doaj +1 more source
ISPRS International Journal of Geo-Information, 2015 Multi-scale/multi-level geographic object-based image analysis (MS-GEOBIA) methods are becoming widely-used in remote sensing because single-scale/single-level (SS-GEOBIA) methods are often unable to obtain an accurate segmentation and classification of ...
Brian A. Johnson +4 more
doaj +1 more source
Earth and Space Science, Volume 10, Issue 5, May 2023., 2023 Abstract Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents.
Aaron E. Maxwell +5 more
wiley +1 more source