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

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.

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

This is a PyTorch model trained on the [Tropical Cyclone Wind Estimation Competition](https://registry.mlhub.earth/10.34911/rdnt.xs53up/) dataset with v0.1 of the [TorchGeo](https://github.com/microsoft/torchgeo) package. The model is a resnet18 model pretrained on [ImageNet](https://www.image-net.org/index.php) then trained with a MSE loss. The data were randomly split 80/20 by storm ID and an early stop was

This is a PyTorch model trained on the [Tropical Cyclone Wind Estimation Competition](https://registry.mlhub.earth/10.34911/rdnt.xs53up/) dataset with v0.1 of the [TorchGeo](https://github.com/microsoft/torchgeo) package. The model is a resnet18 model pretrained on [ImageNet](https://www.image-net.org/index.php) 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.