Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities
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 …
(GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote …
[HTML][HTML] Utilities of artificial intelligence in poverty prediction: a review
Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the
world's society—poverty. Our goal is to investigate utilities of AI in poverty prediction via …
world's society—poverty. Our goal is to investigate utilities of AI in poverty prediction via …
Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
VF Rodriguez-Galiano, M Chica-Olmo… - Remote Sensing of …, 2012 - Elsevier
A Random Forest (RF) classifier was applied to spectral as well as mono-and multi-seasonal
textural features extracted from Landsat TM imagery to increase the accuracy of land cover …
textural features extracted from Landsat TM imagery to increase the accuracy of land cover …
Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines
Crop mapping is one major component of agricultural resource monitoring using remote
sensing. Yield or water demand modeling requires that both, the total surface that is …
sensing. Yield or water demand modeling requires that both, the total surface that is …
Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data
This paper contributes empirical evidence about the usefulness of remote sensing imagery
to quantify the degree of poverty at the intra-urban scale. This concept is based on two …
to quantify the degree of poverty at the intra-urban scale. This concept is based on two …
[HTML][HTML] Exploring the potential of machine learning for automatic slum identification from VHR imagery
Slum identification in urban settlements is a crucial step in the process of formulation of pro-
poor policies. However, the use of conventional methods for slum detection such as field …
poor policies. However, the use of conventional methods for slum detection such as field …
Measuring urban poverty using multi-source data and a random forest algorithm: A case study in Guangzhou
T Niu, Y Chen, Y Yuan - Sustainable Cities and Society, 2020 - Elsevier
Conventional measurements of urban poverty mainly rely on census data or aggregated
statistics. However, these data are produced with a relatively long cycle, and they hardly …
statistics. However, these data are produced with a relatively long cycle, and they hardly …
Remote-sensing image analysis and geostatistics
F Van der Meer - International Journal of Remote Sensing, 2012 - Taylor & Francis
The random function theory forms the basis of geostatistics and allows modelling of the
uncertainty associated with spatial estimation and simulation. Remote sensing involves …
uncertainty associated with spatial estimation and simulation. Remote sensing involves …
Analyzing fine-scale wetland composition using high resolution imagery and texture features
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it
is necessary to employ both accurate and rapid mapping of wet graminoid/sedge …
is necessary to employ both accurate and rapid mapping of wet graminoid/sedge …
Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches
A Dasgupta, S Grimaldi, R Ramsankaran… - Remote sensing of …, 2018 - Elsevier
Abstract Synthetic Aperture Radar (SAR) data are currently the most reliable resource for
flood monitoring, though still subject to various uncertainties, which can be objectively …
flood monitoring, though still subject to various uncertainties, which can be objectively …