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...

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...

The Replicable AI for Microplanning (Ramp) deep learning model is a semantic segmentation one which detects buildings from satellite imagery and delineates the footprints in low-and-middle-income countries (LMICs) using satellite imagery and enables in-country users to build their own deep learning models for their regions of interest. The architecture and approach were inspired by the Eff-UNet model outlined in this CVPR 2020 Paper. read more...