Geoscience and Remote Sensing

The datasets in the compressed file were used in the case study of the article entitled Automated Machine Learning Pipeline for Geochemical Analysis by Germán H. Alférez, et al. Our approach was evaluated with a compositional dataset from 6 fault-separated blocks in the Peninsular Ranges Province and Transverse Ranges Province. The Peninsular Ranges are a group of mountain ranges, stretching from Southern California to Southern Baja California, Mexico. North of the Peninsular Ranges Province is the east-west Transverse Ranges Province.

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Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner. 

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As part of the 2018 IEEE GRSS Data Fusion Contest, the Hyperspectral Image Analysis Laboratory and the National Center for Airborne Laser Mapping (NCALM) at the University of Houston are pleased to release a unique multi-sensor optical geospatial representing challenging urban land-cover land-use classification task. The data were acquired by NCALM over the University of Houston campus and its neighborhood on February 16, 2017 between 16:31 and 18:18 GMT.

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BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.

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The detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), Hewlett Packard Enterprise, SolarAid, and Data Science Experts, aims to promote research in automatic detection of human settlements deprived of access to electricity using multimodal and multitemporal remote sensing data.

Last Updated On: 
Thu, 01/06/2022 - 03:33
Citation Author(s): 
Colin Prieur, Hana Malha, Frederic Ciesielski, Paul Vandame, Giorgio Licciardi, Jocelyn Chanussot, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya

The dataset is a new high-quality dataset to advance sea-land segmentation with high-resolution remote sensing images. The dataset contains 1,726 hand-labeled and cropped Gaofen-1 images with an 8-meter spatial resolution and 4 bands, covering the various types of coastlines in Lianyungang, China.

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Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.

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398 Views

Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from the period 01/07/2020 to 03/11/2020.

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