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Found 7 papers, 2 papers with code
Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach
We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.
Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time.
Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation.
NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function
We have designed a loss function which is an unique combination of weighted sum of Euclidean, neighbourhood, and perceptual loss for training the deep network.
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security.
MRSCAtt: A Spatio-Channel Attention-Guided Network for Mars Rover Image Classification
To necessitate the use of spatial and channel attention, we perform an ablation study to show the effectiveness of each of the components.
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping.
