[HTML][HTML] A review of supervised object-based land-cover image classification

L Ma, M Li, X Ma, L Cheng, P Du, Y Liu - ISPRS Journal of Photogrammetry …, 2017‏ - Elsevier
Object-based image classification for land-cover map** purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …

[HTML][HTML] Sensors, features, and machine learning for oil spill detection and monitoring: A review

R Al-Ruzouq, MBA Gibril, A Shanableh, A Kais… - Remote Sensing, 2020‏ - mdpi.com
Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling

S Georganos, T Grippa, A Niang Gadiaga… - Geocarto …, 2021‏ - Taylor & Francis
Abstract Machine learning algorithms such as Random Forest (RF) are being increasingly
applied on traditionally geographical topics such as population estimation. Even though RF …

Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities

G Chen, Q Weng, GJ Hay, Y He - GIScience & Remote Sensing, 2018‏ - Taylor & Francis
Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis
(GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote …

Very high resolution object-based land use–land cover urban classification using extreme gradient boosting

S Georganos, T Grippa, S Vanhuysse… - … and remote sensing …, 2018‏ - ieeexplore.ieee.org
In this letter, the recently developed extreme gradient boosting (Xgboost) classifier is
implemented in a very high resolution (VHR) object-based urban land use-land cover …

Dynamics of ecosystem services (ESs) in response to land use land cover (LU/LC) changes in the lower Gangetic plain of India

S Talukdar, P Singha, S Mahato, B Praveen… - Ecological …, 2020‏ - Elsevier
The ecosystems provide a range of material as well as non-material services that contribute
to human well-being as well as supply necessary resources for the organisms. The land …

[HTML][HTML] Land cover classification from fused DSM and UAV images using convolutional neural networks

HAH Al-Najjar, B Kalantar, B Pradhan, V Saeidi… - Remote Sensing, 2019‏ - mdpi.com
In recent years, remote sensing researchers have investigated the use of different modalities
(or combinations of modalities) for classification tasks. Such modalities can be extracted via …

[HTML][HTML] Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

A Maiti, Q Zhang, S Sannigrahi, S Pramanik… - Sustainable cities and …, 2021‏ - Elsevier
Since December 2019, the world has witnessed the stringent effect of an unprecedented
global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute …

[HTML][HTML] Recent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review

A Duarte, N Borralho, P Cabral, M Caetano - Forests, 2022‏ - mdpi.com
Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the
last decade to collect data for forest insect pest and disease (FIPD) monitoring. These …