Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms

A Tassi, M Vizzari - Remote Sensing, 2020 - mdpi.com
Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and
object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be …

Land-use mapping for high-spatial resolution remote sensing image via deep learning: A review

N Zang, Y Cao, Y Wang, B Huang… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Land-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs)
is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs …

Mapping roofing with asbestos-containing material by using remote sensing imagery and machine learning-based image classification: A state-of-the-art review

M Abbasi, S Mostafa, AS Vieira, N Patorniti, RA Stewart - Sustainability, 2022 - mdpi.com
Building roofing produced with asbestos-containing materials is a significant concern due to
its detrimental health hazard implications. Efficiently locating asbestos roofing is essential to …

PlanetScope, Sentinel-2, and Sentinel-1 data integration for object-based land cover classification in Google Earth Engine

M Vizzari - Remote Sensing, 2022 - mdpi.com
PlanetScope (PL) high-resolution composite base maps have recently become available
within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between …

Satellite image classification methods and techniques: A survey

H Ouchra, A Belangour - 2021 IEEE International Conference …, 2021 - ieeexplore.ieee.org
At the age of artificial intelligence, remote sensing and especially satellite imagery is gaining
widespread interest among computer science community in their effort to give machines the …

[HTML][HTML] Land use land cover change detection through geospatial analysis in an Indian Biosphere Reserve

TK Thakur, DK Patel, A Bijalwan, MJ Dobriyal… - Trees, Forests and …, 2020 - Elsevier
The present study examines the land use land cover (LULC) and change detection impacts
on forest ecosystem in Achanakmaar Amarkantak Biosphere Reserve (AABR–area 626.76 …

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran

F Aslami, A Ghorbani - Environmental monitoring and assessment, 2018 - Springer
In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in
the Ardabil province in the northwest of Iran, was detected using an object-based method …

Assessing the effect of water scarcity on crop selection and spatial pattern of croplands in central Iran

M Bozorgi, M Moein, F Nejadkoorki, NB Toosi - Computers and Electronics …, 2020 - Elsevier
Farming and food production for the increasing world's population require proper
preparation of a diverse range of environmental parameters especially water availability and …

Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River …

ER da Cunha, CAG Santos, RM da Silva… - Environmental …, 2020 - Springer
In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado
biome has caused severe changes in land use and land cover (LULC), and areas previously …

Monitoring rice lodging grade via Sentinel-2A images based on change vector analysis

Q Sun, X Gu, L Chen, X Xu, Y Pan, X Hu… - International Journal of …, 2022 - Taylor & Francis
Lodging stress influences the yield, quality, and mechanical harvesting ability of rice (Oryza
sativa L.). It is of great significance for the quantitative and objective evaluation of rice yield …