From 6826617e703d4fd477cdf47b2cbc080ab8e5cea7 Mon Sep 17 00:00:00 2001 From: Robin Cole Date: Sat, 4 Sep 2021 06:21:43 +0100 Subject: [PATCH] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f84c7243..d0978aba 100644 --- a/README.md +++ b/README.md @@ -480,10 +480,10 @@ This section contains a short list of datasets relevant to deep learning, partic * SpaceNet - WorldView-3 [article here](https://spark-in.me/post/spacenet-three-challenge), and [semantic segmentation using Raster Vision](https://docs.rastervision.io/en/0.8/quickstart.html) ## Kaggle -Kaggle hosts over > 100 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets). +Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets). The [kaggle blog](http://blog.kaggle.com) is an interesting read. -### Kaggle - Amazon from space - classification challenge +### Kaggle - Amazon from space (classification challenge) * https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data * 3-5 meter resolution GeoTIFF images from planet Dove satellite constellation * 12 classes including - **cloudy, primary + waterway** etc @@ -491,7 +491,7 @@ The [kaggle blog](http://blog.kaggle.com) is an interesting read. * [FastAI Multi-label image classification](https://towardsdatascience.com/fastai-multi-label-image-classification-8034be646e95) * [Multi-Label Classification of Satellite Photos of the Amazon Rainforest](https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/) -### Kaggle - DSTL - segmentation challenge +### Kaggle - DSTL (segmentation challenge) * https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection * Rating - medium, many good examples (see the Discussion as well as kernels), but as this competition was run a couple of years ago many examples use python 2 * WorldView 3 - 45 satellite images covering 1km x 1km in both 3 (i.e. RGB) and 16-band (400nm - SWIR) images @@ -505,13 +505,13 @@ The [kaggle blog](http://blog.kaggle.com) is an interesting read. * Rating - medium, most solutions using deep-learning, many kernels, [good example kernel](https://www.kaggle.com/kmader/baseline-u-net-model-part-1) * I believe there was a problem with this dataset, which led to many complaints that the competition was ruined -### Kaggle - Draper - place images in order of time +### Kaggle - Draper (place images in order of time) * https://www.kaggle.com/c/draper-satellite-image-chronology/data * Rating - hard. Not many useful kernels. * Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned. * Kaggle interviews for entrants who [used XGBOOST](http://blog.kaggle.com/2016/09/15/draper-satellite-image-chronology-machine-learning-solution-vicens-gaitan/) and a [hybrid human/ML approach](http://blog.kaggle.com/2016/09/08/draper-satellite-image-chronology-damien-soukhavong/) -### Kaggle - Deepsat - classification challenge +### Kaggle - Deepsat (classification challenge) Not satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in `.mat` Matlab format. JPEG? * [Imagery source](https://csc.lsu.edu/~saikat/deepsat/) * [Sat4](https://www.kaggle.com/crawford/deepsat-sat4) 500,000 image patches covering four broad land cover classes - **barren land, trees, grassland and a class that consists of all land cover classes other than the above three**