Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data

Real-time detection of wildlife using NOAA/AVHRR data Study area :(Kayamaki Wildlife Refuge)

Forest fire in recent years has paid great attention to climate change and ecosystems. Remote sensing is a quick and inexpensive way to detect and monitor forest fires on a large scale. The purpose of this study was to identify forest and rangeland fire hazards using NOAA / AVHRR in Kayamaki Wildlife Refuge. For the purpose of this study, the history of the fire-burns occurred in MODIS products...

Near Real-time Object Detection in RGBD Data

Most methods of object detection with RGBD cameras set hard constraints on their operational area. They only work with specific objects, in specific environments, or rely on time consuming computations. In the context of home robotics, such hard constraints cannot be made. Specifically, an autonomous home robot shall be equipped with an object detection pipeline that runs in near real-time and ...

Detection of Congenital Mullerian Anomalies Using Real-Time 3D Sonography

Near Real-Time Processing of Proteomics Data Using Hadoop.

This article presents a near real-time processing solution using MapReduce and Hadoop. The solution is aimed at some of the data management and processing challenges facing the life sciences community. Research into genes and their product proteins generates huge volumes of data that must be extensively preprocessed before any biological insight can be gained. In order to carry out this process...

Near Real-Time Disturbance Detection Using Satellite Image Time Series: Drought Detection in Somalia

Near real-time monitoring of ecosystem disturbances is critical for rapidly assessing and addressing impacts on carbon dynamics, biodiversity, and socio-ecological processes. Satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a ...