Deep Learning

This dataset contains the Supplementary Information of the article "Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: a new methodology to identify viral features" (Manuscript DOI: 10.1109/ACCESS.2023.3311752).

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection of the blur anomalies in the video is crucial to these systems. In this research, a learning-based approach for camera blur detection is proposed.

AndalUnmixingRGB is a Sentinel-2 satellite digital RGB imagery enriched with environmental ancillary data and designed for blind spectral unmixing using deep learning. Generally, spectral unmixing involves two main tasks: spectral signature identification of different available land use/cover types in the analyzed hyperspectral or multispectral imagery (endmember identification task) and their respective proportions measurement (abundance estimation task).

Most of the current smoke detection techniques are developed using CCTV images. To identify the wildfire early, it can be useful to deploy satellite imagery and develop models that can recognize smoke in forest areas. However, very few labelled satellite image datasets are available to build the wildfire smoke detection model. In order to find a solution to this problem, a dataset consisting 23, 644 satellite images was gathered. The dataset is divided into four categories: smoke, smoke with fog, non-smoke, and non-smoke with fog.