bao18 / open_earth_map Goto Github PK
View Code? Open in Web Editor NEWQuick start in OpenEarthMap
License: MIT License
Quick start in OpenEarthMap
License: MIT License
Hi, Bruno
Could you provide a list about the origanization of the dataset? I mean I want to know where does the each images come from.
Could you release a list to index each images to their origin dataset?
There is a interesting question about the UDA task. Do you think the dataset gap(like different satellite) is a domain problem? Will it make a more realistic UDA task for a real application?
I downloaded the dataset from the Zenodo link. It seems like only the Training and Validation splits are included.
Is it possible the request the test set labels ? Thank you!
this line should be == 2
instead of != 2
Hello, I have a question about submission. Should we put all the pre-edited images in the validation data in a .zip file? THANKS
Hello, @bao18 . This dataset is great. But when I use U-Net+efficientb4, I can only obtain 60% IoU, which is far from the 68% as reported in your paper. Do you know why it happens?
Settings: the training parameters are the same as demo_training.py
and the only difference is the model.
Colab's network is not friendly to me, so can you make python(.py) on computer?
Can you please add the code how to output the result?
thanks
Hello,
I wanted to ask about the projection information for the labels.
The label GeoTIFFs contain information with the EPSG:4326 CRS (WGS 84). When I render these with respect to other image datasets, I am seeing a misalignment. I tried playing around with linear displacements and it appears to require a more complex reprojection to account for the misalignment.
Based on Arxiv pre-print, I believe this is probably the result of precisely aligning the label with the reference imagery. Is that the case, or do I just have the projection data messed up?
Thank you! This is an incredible dataset!
Hey I am still having issues setting up the dataset correctly. When I try to execute the script to compile the images from xDB I am just getting thrown an error:
(openearthmap) D:\open_earth_map-main>python data/compile_xbd.py --path_to_OpenEarthMap "D:/OpenEarthMap_wo_xBD" --path_to_xBD "D:/xview2_geotiff/xview2_geotiff"
Traceback (most recent call last):
File "D:\open_earth_map-main\data\compile_xbd.py", line 36, in
idx = np.where(xbd_fullimgs[:, 0] == f1)[0]
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
The readme says:
The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.
But I see nothing there but pairs. No further explanation.
@bao18 Can you help me please?
After I carefully prepared all the requested data, I found label file for xBD images missing. The amount of label file in downloaded "OpenEarthMap_wo_xBD" folder is 3500. Where can I get them?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.