Sensors, features, and machine learning for oil spill detection and monitoring: A review
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 …
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …
Change detection in remote sensing image data comparing algebraic and machine learning methods
A Goswami, R Luciani, G Laneve, M JahJah - IEEE journal of selected topics …, 2019 - ieeexplore.ieee.org
Agricultural activities conducted in the Great Rift Valley of Kenya show a significant decline
of productivity levels. This phenomenon is mainly related to limited availability of water …
of productivity levels. This phenomenon is mainly related to limited availability of water …
Spatiotemporal and spectral analysis of sand encroachment dynamics in southern Tunisia
Aeolian processes in drylands often transcend into sand encroachment, a common form of
land degradation. Highly reflective desert features, hence sandy areas, often cause spectral …
land degradation. Highly reflective desert features, hence sandy areas, often cause spectral …
Using classification trees to predict forest structure types from LiDAR data
This study assesses whether metrics extracted from airborne LiDAR (Light Detection and
Ranging) raw point cloud can be exploited to predict different forest structure types by …
Ranging) raw point cloud can be exploited to predict different forest structure types by …
Land use/cover classification techniques using optical remotely sensed data in landscape planning
O Şatır, S Berberoğlu - Landscape Planning. Rijeka: InTech, 2012 - books.google.com
The observed biophysical cover of the earth's surface, termed land-cover is composed of
patterns that occur due to a variety of natural and human-derived processes. On the other …
patterns that occur due to a variety of natural and human-derived processes. On the other …