Merge pull request satellite-image-deep-learning#34 from BloodAxe/master

Added link to xView3 solution

README.md

@@ -713,6 +713,7 @@ Detecting the most noticeable or important object in a scene
 * [LEVIR-Ship](https://github.com/WindVChen/LEVIR-Ship) -> a dataset for tiny ship detection under medium-resolution remote sensing images
 * [Push-and-Pull-Network](https://github.com/WindVChen/Push-and-Pull-Network) -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images
 * [DRENet](https://github.com/WindVChen/DRENet) -> code for 2022 [paper])(https://ieeexplore.ieee.org/abstract/document/9791363): A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset
+* [xView3-The-First-Place-Solution](https://github.com/BloodAxe/xView3-The-First-Place-Solution) - A winning solution for [xView 3](https://iuu.xview.us/) challenge (Vessel detection, classification and length estimation on Sentinetl-1 images). Contains trained models, inference pipeline and training code & configs to reproduce the results.
 
 #### Object detection - cars, vehicles & trains
 * [Truck Detection with Sentinel-2 during COVID-19 crisis](https://github.com/hfisser/Truck_Detection_Sentinel2_COVID19) -> moving objects in Sentinel-2 data causes a specific reflectance relationship in the RGB, which looks like a rainbow, and serves as a marker for trucks. Improve accuracy by only analysing roads. Not using object detection but relevant. Also see [S2TD](https://github.com/hfisser/S2TD)