satellite-image-deep-learning / techniques Goto Github PK

Many github links return Status: 429

Todo: ignore these

Great list, have suggested it to multiple people that are looking for DL+Geo ressources.

Solaris should def. be added here, great tool! DL framework for remote sensing data a bit similar to rastervision but with some unique very vool features! https://github.com/CosmiQ/solaris

On mobile for some time so no PR sorry :)

To catch bad links. Options

• https://github.com/marketplace/actions/markdown-link-check
• https://github.com/marketplace/actions/link-checker

Dear Sir,
According to our last conversation, you were looking for a collapsible markdown format for the readme file. A format is mentioned in the github doc. By using the <details> tag, it is possible to create dropdown contents. I have tried to change the font styles within the <summary> tag, but did not work.

AWS sponsors access

ERROR: 13 dead links found!
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[✖] https://spacenetchallenge.github.io/datasets/datasetHomePage.html → Status: 404
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[✖] http://srtm.csi.cgiar.org/srtmdata/ → Status: 0
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[✖] https://neat-EO.pink → Status: 0
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[✖] https://onlinelibrary.wiley.com/doi/10.1002/9781118724194.ch11 → Status: 403
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[✖] https://www.esa.int/Applications/Observing_the_Earth/Monitoring_European_air_traffic_with_Earth_observation → Status: 403
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[✖] https://elib.dlr.de/128117/1/SEN12MS_Preprint.pdf → Status: 401
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[✖] https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JB017519 → Status: 403
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[✖] https://moderndata.plot.ly/weather-maps-in-python-with-mapbox-gl-xarray-and-netcdf4/ → Status: 500
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[✖] https://platform.digitalglobe.com/gbdx/ → Status: 404
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[✖] https://course-v3.fast.ai/index.html → Status: 0
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[✖] https://github.com/microsoft/aerial_wildlife_detection/tree/multiProject → Status: 404
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[✖] https://www.datacamp.com/courses/working-with-geospatial-data-in-python?tap_a=5644-dce66f&tap_s=411670-1f1ebc → Status: 403
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[✖] https://www.linkedin.com/in/robmarkcole/ → Status: 999

I want to add "DC-GANs" technique in "18. Generative Adversarial Networks (GANs)", with GitHub repo. & Research paper reference. Please create a PR for the same. Thank you.

We have recently released an updated version of the SEN1-2 dataset, it contains the same regions, but we have added all the S2 multispectral bands, All S1 bands as well as corresponding MODIS land cover.

Paper: https://elib.dlr.de/128117/1/SEN12MS_Preprint.pdf
Data: https://mediatum.ub.tum.de/1474000

It is now the largest, manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover mapping, SAR-optical fusion, segmentation and classification tasks

The README file in this repo has some bad links - [404:NotFound]

The markup version of the readme that is displayed for the main page in this repo contains the following links:
Status code [404:NotFound] - Link: https://www.kaggle.com/robmarkcole/satellite-image-classification (Example notebook)
Status code [404:NotFound] - Link: http://scikit-image.org/docs/dev/auto_examples/transform/plot_register_translation.html?highlight=cross%20correlation (Phase correlation in scikit-image)
Status code [404:NotFound] - Link: https://developers.planet.com/docs/quickstart/getting-started/ (API key)
Status code [404:NotFound] - Link: https://github.com/galiboo/olympus

(The link in the readme’s raw markdown may appear in a different format to the link above)

Theses bad links were found by a tool I very recently created as part of an new experimental hobby project: https://github.com/MrCull/GitHub-Repo-ReadMe-Dead-Link-Finder
I (a human) verified that this link is broken and have manually logged this Issue (i.e. this Issue has not been created by a bot).
If this has been in any way helpful then please consider giving the above Repo a Star.

Hi robmarkcole,

I was trying to implement the tutorial on YOLOv5 Object Detection for RarePlanes : https://github.com/jeffaudi/rareplanes-yolov5
But I found a bug which make one part of the tutorial no longer useable. It's related with AWS and CosmiQ.

The problem appear in step 3 of the A. AMI/EC2 part, when trying to find the AMI CosmiQ_YOLO_Planes. Which it was impossible to find. Here you can have a look at my AWS AMI search.

After some research I found out that CosmiQ has been closed down since March 2021 : https://medium.com/the-downlinq/closing-time-cosmiq-works-is-closing-down-and-ending-its-leadership-of-spacenet-a53ba239745b
Therfore, all of the AMI they provided have been removed. So this part of the tutorial is not longer useable.

However, I could still download the RarePlanes dataset from the s3Bucket to work locally and accomplish the second part : B. On your own GPUs.

Thanks for your help with this wonderful repo,

Alban Tchikladzé

Currently datasets live there but that is not the main focus of this repo

Appears I have so many github links the Ci is raising Status: 429 codes and failing the run

Object Segmentation Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (https://github.com/Z-Zheng/FarSeg) (CVPR 2020).
Hyperspectral Image Segmentation FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification (https://github.com/Z-Zheng/FreeNet) (TGRS 2020).
All-Weather Mapping Deep multisensor learning for missing-modality all-weather mapping (https://www.sciencedirect.com/science/article/pii/S0924271620303476) (ISRPS P&RS 2021).
Object Detection HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery (https://www.sciencedirect.com/science/article/pii/S0924271620301167) (ISRPS P&RS 2020)

👋 Hi @robmarkcole thanks so much for compiling these resources. Just wanted to let you know that PEARL is publicly available for free. Could it maybe please be moved from the useful paid software section into the semantic segmentation section?

Hello! I want to convert the optical images of SEN1-2 to grayscale and then to dB scale. However, using just the dB formula (20*log10) gives much brighter images than the SAR ones.

Can you please describe how was the dB scale of SAR images calculated?

• Land use classification using Keras and UC Merced dataset
• Land use classification of UC Merced dataset using Keras or alternatively fastai

I'm looking for DSM satalllite images, is there any of them?

An additional dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset. Contains around 250K corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data.

Data download: https://mediatum.ub.tum.de/1436631
Paper: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1/141/2018/isprs-annals-IV-1-141-2018.pdf

It would be nice to hit a button and expand all links, so the page could be searched without needing to dive into the raw markdown

[✖] https://github.com/aws-samples/amazon-rekognition-video-analyzer → Status: 429
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[✖] https://github.com/RemotePixel/remotepixel-api → Status: 429
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[✖] https://github.com/zarr-developers/zarr → Status: 429
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[✖] https://github.com/PacktPublishing/-Practical-Deep-Learning-on-the-Cloud → Status: 429
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[✖] https://github.com/jrosebr1/simple-keras-rest-api → Status: 429
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[✖] https://github.com/pytorch/serve → Status: 429
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[✖] https://github.com/ryfeus/amazon-rekognition-custom-labels-satellite-imagery → Status: 429
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[✖] https://github.com/developmentseed/chip-n-scale-queue-arranger → Status: 429
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[✖] https://github.com/developmentseed/fastai-serving → Status: 429
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[✖] https://github.com/Esri/raster-deep-learning → Status: 429

ICCV21, Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

RSE21, Building Damage Assessment for Rapid Disaster Response with a Deep Object-based Semantic Change Detection Framework: from natural disasters to man-made disasters

ISPRS P&RS 22, ChangeMask: Deep Multi-task Encoder-Transformer-Decoder Architecture for Semantic Change Detection