Onera Satellite Change Detection
# Nickname Score [%]
1 m_mih 96.13
2 m_mih 96.12
3 Maria_Pap6 96.10
4 Maria_Pap6 96.10
5 m_mih 96.10
6 Papad 96.09
7 Maria_Pap6 96.09
8 Maria_Pap6 96.09
9 m_inr 96.08
10 mamm2 96.06
2018 IEEE GRSS Data Fusion Contest: Data Fusion Classification Challenge
# Nickname Score [%]
1 Gaussian 80.78
2 Gaussian 80.77
3 dlrpba 80.74
4 dlrpba 80.46
5 dlrpba 80.31
6 AGTDA 79.79
7 Gaussian 79.34
8 IPIU 79.23
9 WULA2 78.77
10 WULA2 78.73
2018 IEEE GRSS Data Fusion Contest: Multispectral LiDAR Classification Challenge
# Nickname Score [%]
1 Gaussian 81.07
2 Gaussian 79.07
3 WULA2 78.67
4 WULA2 78.39
5 AGTDA 78.05
6 IPIU 78.01
7 IPIU 77.24
8 WULA2 77.16
9 GaoLei 75.82
10 AGTDA 75.43
2018 IEEE GRSS Data Fusion Contest: Hyperspectral Classification Challenge
# Nickname Score [%]
1 leland 100.00
2 challenger 77.39
3 WULA2 76.59
4 XudongKang 76.37
5 XudongKang 76.15
6 XudongKang 76.12
7 XudongKang 76.00
8 XudongKang 75.97
9 WULA 75.33
10 WULA 75.08
2017 IEEE GRSS Data Fusion Contest
# Nickname Score [%]
1 tester 80.95
2 tester 79.22
3 tester 79.22
4 tester 78.65
5 navia 78.15
6 tester 77.85
7 navia 77.63
8 NAVIA 76.15
9 navia 75.87
10 WXYZ 74.94
2015 IEEE GRSS Data Fusion Contest Dataset: Zeebruges
# Nickname Score [%]
1 rit1 87.93
2 rit1 87.91
3 RIT_YS 87.85
4 nshaud 87.31
5 RIT_YS 85.50
6 rit1 85.22
7 RIT_YS 85.22
8 rit1 84.82
9 rit1 83.63
10 rit1 83.51
San Francisco
# Nickname Score [%]
1 carranza96 99.37
2 mcg96 99.31
3 mcg96 99.03
4 carranza96 98.70
5 mcg96 98.64
6 carranza96 98.62
7 mcg96 98.09
8 carranza96 96.81
9 mcg96 96.49
10 carranza96 96.28
Flevoland
# Nickname Score [%]
1 leland 100.00
2 carranza96 99.05
3 mcg96 99.00
4 mcg96 98.96
5 carranza96 98.79
6 mcg96 98.51
7 mcg96 98.43
8 mcg96 98.35
9 mcg96 98.02
10 carranza96 97.79
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 ZouSR 86.64
2 Test 86.64
3 Test 86.64
4 EonRehman 85.34
5 ZouSR 84.90
6 Test 84.90
7 mancargar6 84.79
8 Passer-by 84.42
9 Passer-by 84.12
10 Passer-by 84.12
Indian Pines
# Nickname Score [%]
1 LiyingGao 99.31
2 taoorwell 99.21
3 testyj 99.19
4 Xuexz 98.93
5 Xuexz 98.72
6 Test 98.72
7 mama 97.37
8 carranza96 95.53
9 EonRehman 95.26
10 EonRehman 94.64

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.