DiegoABR07
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An Android app that runs a scikit-learn model converted to the ONNX format
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
My computer vision project to detect each face of a Rubik's cube.
Cube Detection using YOLOv5 with Oriented Bounding Boxes (OBB)
Sign Language Segmentation with Temporal Convolutional Networks (ICASSP'21) and Sign Segmentation with Changepoint-Modulated Pseudo-Labelling (CVPRW'21)
codes for RS paper: Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal
Performing Satellite Image Segmentation using SegNet algorithm.
Running Segment Anything Geospatial over Earth Engine exports with Dataflow at scale.
Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery…
pytorch,unet,aerial image,segnet,pspnet,satellite,segmentaion
Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)
Deep Convolutional Encoder-Decoder network for image segmentation
UNet++, UNet, SegNet and DeepLabv3 implemented in Keras for MoNuSeg dataset
U Net Implementation from Scratch using TensorFlow (oxford iiit pet dataset
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image …
The code is associated with the following paper "A Fast and Compact 3-D CNN for Hyperspectral Image Classification". IEEE Geoscience and Remote Sensing Letters
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
3D U-Net model for volumetric semantic segmentation written in pytorch
Semantic segmentation of satellite imagery using U-nets (U-nets: https://arxiv.org/abs/1505.04597)
This is the code of the paper Multiple Spectral Resolution 3D Convolutional Neural Network for Hyperspectral Image Classification. And the paper has been accpeted by remote sensing.
Leveraging Segment-Anything Model (SAM) to delineate crop field boundaries on Sentinel-2 images
End-to-end workflow for generating high resolution cropland maps
Techniques for deep learning with satellite & aerial imagery
🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis
Multi-temporal Super-Resolution on Sentinel-2 Imagery using Deimos
Code & trained network files of FCNs to delineate agricultural field boundaries