Onera Satellite Change Detection
# Nickname Score [%]
1 APX2 88.02
2 StephenApX 87.94
3 StephenApX 87.89
4 StephenApX 87.89
5 StephenApX 87.30
6 APX2 87.14
7 StephenApX 87.02
8 APX2 86.62
9 APX2 86.62
10 APX2 86.01
2018 IEEE GRSS Data Fusion Contest: Data Fusion Classification Challenge
# Nickname Score [%]
1 dlrpba 76.32
2 AGTDA 76.15
3 dlrpba 76.07
4 challenger 75.99
5 dlrpba 75.91
6 challenger 75.79
7 challenger 75.79
8 WULA2 74.94
9 WULA2 74.63
10 IPIU 74.42
2018 IEEE GRSS Data Fusion Contest: Multispectral LiDAR Classification Challenge
# Nickname Score [%]
1 Gaussian 72.01
2 WULA2 71.86
3 AGTDA 71.54
4 WULA2 71.00
5 IPIU 70.83
6 GaoLei 70.27
7 Gaussian 70.05
8 WULA2 70.05
9 AGTDA 69.99
10 IPIU 69.97
2018 IEEE GRSS Data Fusion Contest: Hyperspectral Classification Challenge
# Nickname Score [%]
1 challenger 75.79
2 challenger 72.28
3 XudongKang 71.59
4 XudongKang 71.40
5 XudongKang 71.37
6 XudongKang 71.28
7 XudongKang 71.25
8 dlrpba 70.96
9 timespace 70.22
10 WULA2 69.56
2017 IEEE GRSS Data Fusion Contest
# Nickname Score [%]
1 achanhon 0.00
2 aboulch 0.00
3 Ahmed 0.00
4 HuiYang 0.00
5 J-BIDLC 0.00
6 J-BIDLC 0.00
7 XDJteam 0.00
8 XDJteam 0.00
9 XDJteam 0.00
10 J-BIDLC 0.00
2015 IEEE GRSS Data Fusion Contest Dataset: Zeebruges
# Nickname Score [%]
1 rit1 77.04
2 rit1 76.97
3 nshaud 76.33
4 rit1 75.05
5 RIT_YS 75.05
6 RIT_YS 74.47
7 rit1 74.43
8 rit1 73.27
9 RIT_YS 73.08
10 rit1 72.47
San Francisco
# Nickname Score [%]
1 mcg96 97.71
2 mcg96 97.51
3 carranza96 97.41
4 mcg96 97.19
5 mcg96 96.42
6 carranza96 96.14
7 carranza96 95.22
8 mcg96 92.12
9 carranza96 91.62
10 Adil 87.76
Flevoland
# Nickname Score [%]
1 mcg96 96.05
2 carranza96 95.52
3 carranza96 94.63
4 mcg96 93.89
5 mcg96 93.81
6 mcg96 92.64
7 carranza96 92.15
8 mcg96 92.15
9 mcg96 90.25
10 mcg96 90.16
Avon12
# Nickname Score [%]
1 yxl7245 95.35
2 yxl7245 95.35
3 yilong_rit 95.35
4 Nirmalan 95.19
5 EonRehman 95.08
6 EonRehman 95.08
7 EonRehman 95.08
8 pete 94.86
9 ST_IE 94.75
10 Tania 94.74
Pavia
# Nickname Score [%]
1 taoorwell 84.14
2 taoorwell 82.32
3 taoorwell 82.17
4 Passer-by 81.27
5 taoorwell 81.14
6 Passer-by 81.12
7 Passer-by 81.12
8 ZouSR 81.00
9 Test 81.00
10 Test 81.00
Indian Pines
# Nickname Score [%]
1 taoorwell 99.68
2 testyj 98.83
3 Xuexz 98.81
4 Test 98.81
5 mama 97.28
6 LiyingGao 92.31
7 carranza96 87.09
8 EonRehman 86.87
9 EonRehman 85.90
10 mancargar6 85.24

GRSS Data and Algorithm Standard Evaluation website

Overview

The Standardized Remote Sensing Data Website of the IEEE Geoscience and Remote Sensing Society (GRSS) provides a set of community data sets and algorithm evaluation standards for use by the Earth observation community to support research, development, and testing of algorithms for remote sensing data products.

The website is aimed at remote sensing scientists, students, and professionals, who wish to evaluate the performances of their image analysis methods on freely available data against undisclosed test samples.

Currently, automated online evaluation of classification results from sample hyperspectral datasets is supported. The system is envisioned to grow to encompass more remote sensing modalities and types of processing results.

The website has been developed within the Standardized Algorithm and Data Evaluation Working Group of the GRSS Image Analysis and Data Fusion Technical Committee.

How to get the data and submit results

  • Register for an account and log in with your username and password.
  • Download the remotely sensed dataset you wish to classify along with the associated training data.
  • Apply your processing methods to generate a classification map and upload it to the website.
  • The website will automatically compute a set of classification accuracy parameters with respect to undisclosed test samples.
  • The top-10 results for each data set are reported on the website. You can also check your personal stats by logging in with your username and password.

Further information and useful hints from other users of this web site can be obtained through the LinkedIn Group of the IADF TC

Best displayed using Mozilla Firefox or Google Chrome. To navigate through the website, it is recommended to use the buttons on the right panel and not the back key.