Radiant MLHub
satellite

A Spatio-Temporal Deep Learning-Based Crop Classification Model for Satellite Imagery

First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020. The model architecture consists of 3-layer Conv-net, Masked Features Averaging layer, 3-layer Bi-directional GRU-net and fully connected classification layer. Masked Features Averaging layer is similar to global average pooling but only averages pixels belong to crop field. read more...
ml-model
cnn
segmentation
supervised

Tropical Cyclone Wind Estimation Model

This is a PyTorch model trained on the Tropical Cyclone Wind Estimation Competition dataset with v0.1 of the TorchGeo package. The model is a resnet18 model pretrained on ImageNet then trained with a MSE loss. The data were randomly split 80/20 by storm ID and an early stop was used based on performance. read more...
ml-model
resnet18
regression
supervised