Merge pull request #1 from RitwikGupta/xview-updates

Update xView references

README.md

@@ -902,7 +902,7 @@ Generally speaking, change detection methods are applied to a pair of images to
 * [DSFANet](https://github.com/wwdAlger/DSFANet) -> code for 2018 [paper](https://arxiv.org/abs/1812.00645): Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
 * [FCD-GAN-pytorch](https://github.com/Cwuwhu/FCD-GAN-pytorch) -> Fully Convolutional Change Detection Framework with Generative Adversarial Network (FCD-GAN) is a framework for change detection in multi-temporal remote sensing images
 * [DARNet-CD](https://github.com/jimmyli08/DARNet-CD) -> code for 2022 paper: A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images
-* [xView2_FDNY](https://github.com/DIUx-xView/xView2_FDNY) -> Damage assessment using pre and post orthoimagery. Fifth-place model from the DIU xView2 competition
+* [xView2_Vulcan](https://github.com/RitwikGupta/xView2-Vulcan) -> Damage assessment using pre and post orthoimagery. Modified + productionized model based off the first-place model from the xView2 challenge.
 * [ESCNet](https://github.com/Bobholamovic/ESCNet) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9474911): An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images
 * [ForestCoverChange](https://github.com/annusgit/ForestCoverChange) -> Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery
 * [deforestation-detection](https://github.com/vldkhramtsov/deforestation-detection) -> code for 2020 paper: DEEP LEARNING FOR HIGH-FREQUENCY CHANGE DETECTION IN UKRAINIAN FOREST ECOSYSTEM WITH SENTINEL-2
@@ -1450,7 +1450,7 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 * [Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training](https://github.com/sidgan/ETCI-2021-Competition-on-Flood-Detection)
 * [SpaceNet_SAR_Buildings_Solutions](https://github.com/SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions) -> The winning solutions for the SpaceNet 6 Challenge
 * [Mapping and monitoring of infrastructure in desert regions with Sentinel-1](https://github.com/ESA-PhiLab/infrastructure)
-* [xView3](https://iuu.xview.us/) is a competition to detect dark vessels using computer vision and global SAR satellite imagery. [The-First-Place-Solution](https://github.com/BloodAxe/xView3-The-First-Place-Solution) and [second place solution](https://github.com/selimsef/xview3_solution). [sar_vessel_detect](https://github.com/allenai/sar_vessel_detect)
+* [xView3](https://iuu.xview.us/) is a competition to detect dark vessels using computer vision and global SAR satellite imagery. [First place solution](https://github.com/DIUx-xView/xView3_first_place) and [second place solution](https://github.com/DIUx-xView/xView3_second_place). Additional places up to fifth place are available at the (xView GitHub Organization page)[https://github.com/DIUx-xView/].
 * [Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition](https://github.com/drivendataorg/stac-overflow)
 * [deSpeckNet-TF-GEE](https://github.com/adugnag/deSpeckNet-TF-GEE) -> implementation of the paper 'deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling'
 * [cnn_sar_image_classification](https://github.com/diogosens/cnn_sar_image_classification) -> CNN for classifying SAR images of the Amazon Rainforest
@@ -1696,7 +1696,7 @@ This section contains a short list of datasets relevant to deep learning, partic
 ## xView Challenge Datasets for Humanitarian Assistance and Disaster Response
 * [xView1](http://xviewdataset.org/) - Objects in context for overhead imagery. A fine-grained object detection dataset with 60 object classes along an ontology of 8 class types. Over 1,000,000 objects across over 1,400 km^2 of 0.3m resolution imagery. Paper available on [arXiv](https://arxiv.org/abs/1802.07856).
 * [xView2/xBD](https://xview2.org/) - Finding and assessing damaged buildings on pre- and post-natural disaster imagery. With over 850,000 annotated buildings across over 45,000 km^2 of 0.3m resolution imagery, this dataset provides precise segmentation masks and damage labels on a four-level spectrum. Paper available on [arXiv](https://arxiv.org/abs/1911.09296).
-* [xView3](https://iuu.xview.us/) - Detecting dark vessels engaged in illegal, unreported, and unregulated (IUU) fishing activities on synthetic aperture radar (SAR) imagery. With human and algorithm annotated instances of vessels and fixed infrastructure across 43,200,000 km^2 of Sentinel-1 imagery, this multi-modal dataset enables algorithms to detect and classify dark vessels.
+* [xView3](https://iuu.xview.us/) - Detecting dark vessels engaged in illegal, unreported, and unregulated (IUU) fishing activities on synthetic aperture radar (SAR) imagery. With human and algorithm annotated instances of vessels and fixed infrastructure across 43,200,000 km^2 of Sentinel-1 imagery, this multi-modal dataset enables algorithms to detect and classify dark vessels. Paper available on [arXiv](https://arxiv.org/abs/2206.00897).
 * All reference code, dataset processing utilities, and winning model codes + weights are available on the (xView GitHub organization page)[https://github.com/DIUx-xView).
 
 ## Vehicle Detection in Aerial Imagery (VEDAI)
@@ -2931,7 +2931,7 @@ For a full list of companies, on and off Github, checkout [awesome-geospatial-co
 * [CARTO](https://github.com/CartoDB) -> "The leading platform for Location Intelligence and Spatial Data Science"
 * [Cervest](https://github.com/Cervest) -> Climate Intelligence
 * [Citymapper](https://github.com/citymapper)
-* [Defense Innovation Unit (DIU)](https://github.com/DIUx-xView) -> run the xView challenges
+* [Defense Innovation Unit (DIU)](https://diu.mil/) -> run the xView challenges
 * [Development Seed](https://github.com/developmentseed)
 * [Descartes Labs](https://github.com/descarteslabs)
 * [Dymaxion Labs](https://github.com/dymaxionlabs)