rs-fusion-datasets 0.9.2.dev3

Download and load Houston 2013 Dataset

Remote Sensing Datasets for Joint Classification of Hyperspectral and LiDAR data

Download and load remote sensing datasets easily and swiftly. rs-fusion-datasets (formerly known as fetch_houston2013) is:

1. A fast houston2013 muufl and trento dataset fetcher that automatically downloads all data
2. A ready-to-use torch dataloader for houston2013 muufl and trento dataset
3. A toolbox for visualizing the datasets

Supported Datasets

1. Houston 2013
2. Trento
3. Muufl
4. Houston 2018 (Working in progress)
5. Augsberg (Working in progress)
6. Berlin (Working in progress)

Quick Start

1. Install this package

pip install rs-fusion-datasets

2. import and get the dataset

from rs_fusion_datasets import fetch_houston2013, fetch_muufl, fetch_trento, split_spmatrix
# For Houston 2013
hsi, dsm, train_label, test_label, info = fetch_houston2013()
# For Muufl
casi, lidar, truth, info = fetch_muufl()
train_label, test_label = split_spmatrix(truth, 20)
# For Trento
casi, lidar, truth, info = fetch_trento()
train_label, test_label = split_spmatrix(truth, 20)

3. Tips: train_label and test_label are sparse matrix, you can either convert them to np.array easily by

train_label=train_label.todense()
test_label =test_label.todense()

or directly use them for getting the value in a very fast way:

    def __getitem__(self, index):
      i = self.truth.row[index]
      j = self.truth.col[index]
      label = self.truth.data[index].item()
      x_hsi = self.hsi[:, i, j]
      x_dsm = self.dsm[:, i, j]
      return x_hsi, x_dsm, label

torch

Ready-to-use Torch vison datasets.

from rs_fusion_datasets import Houston2013, Trento, Muufl
dataset = Muufl(subset='train', patch_size=11)
x_h, x_l, y, extras = dataset[0]

utils

1. lbl2rgb: convert the label dataset to rgb image
2. read_roi: read exported .txt file of ENVI roi to sparse matrix
3. split_spmatrix: split a sparse to get the train dataset and test dataset

Help

• User Manual
• Developer Manual
• Test cases
• PyTorch Demo: train your model in 50 lines of code

Star History

Contribution

We welcome all contributions, including issues, pull requests, feature requests and discussions.

Credits

Houston2013 dataset: https://machinelearning.ee.uh.edu/?page_id=459
paperswithcode: https://paperswithcode.com/dataset/houston
Muufl dataset: https://github.com/GatorSense/MUUFLGulfport
Dafault url of Trento dataset is https://github.com/tyust-dayu/Trento/tree/b4afc449ce5d6936ddc04fe267d86f9f35536afd
The 2013_IEEE_GRSS_DF_Contest_Samples_VA.txt in this repo is exported from original 2013_IEEE_GRSS_DF_Contest_Samples_VA.roi.
Note: If this data is used in any publication or presentation the following reference must be cited:
P. Gader, A. Zare, R. Close, J. Aitken, G. Tuell, “MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set,” University of Florida, Gainesville, FL, Tech. Rep. REP-2013-570, Oct. 2013.
If the scene labels are used in any publication or presentation, the following reference must be cited:
X. Du and A. Zare, “Technical Report: Scene Label Ground Truth Map for MUUFL Gulfport Data Set,” University of Florida, Gainesville, FL, Tech. Rep. 20170417, Apr. 2017. Available: http://ufdc.ufl.edu/IR00009711/00001.
If any of this scoring or detection code is used in any publication or presentation, the following reference must be cited:
T. Glenn, A. Zare, P. Gader, D. Dranishnikov. (2016). Bullwinkle: Scoring Code for Sub-pixel Targets (Version 1.0) [Software]. Available from https://github.com/GatorSense/MUUFLGulfport/.

License

Copyright 2025 songyz2023

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.