Onera Satellite Change Detection
| # |
Nickname |
Score [%] |
| 1 |
Aryan |
100.00 |
| 2 |
RuiYanAI |
88.95 |
| 3 |
DH_RSIA |
88.49 |
| 4 |
ZJU_MPA |
88.31 |
| 5 |
mraz |
88.27 |
| 6 |
DH_NPIA |
88.20 |
| 7 |
antill |
88.13 |
| 8 |
DLUT |
88.13 |
| 9 |
APX2 |
88.02 |
| 10 |
StephenApX |
87.94 |
2018 IEEE GRSS Data Fusion Contest: Data Fusion Classification Challenge
| # |
Nickname |
Score [%] |
| 1 |
meanqqq |
78.22 |
| 2 |
dlrpba |
76.32 |
| 3 |
AGTDA |
76.15 |
| 4 |
dlrpba |
76.07 |
| 5 |
challenger |
75.99 |
| 6 |
dlrpba |
75.91 |
| 7 |
challenger |
75.79 |
| 8 |
challenger |
75.79 |
| 9 |
meanqqq |
75.27 |
| 10 |
WULA2 |
74.94 |
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 |
Transformer |
84.59 |
| 2 |
Transformer |
83.25 |
| 3 |
Transformer |
82.99 |
| 4 |
Transformer |
82.69 |
| 5 |
Djacky |
81.83 |
| 6 |
quanquans1225 |
81.73 |
| 7 |
Drdady2 |
81.18 |
| 8 |
Penn |
80.42 |
| 9 |
Penn |
80.38 |
| 10 |
Djacky |
80.24 |
San Francisco
| # |
Nickname |
Score [%] |
| 1 |
mcg96 |
97.71 |
| 2 |
mcg96 |
97.51 |
| 3 |
carranza96 |
97.41 |
| 4 |
ignaciomasari24 |
97.33 |
| 5 |
ignaciomasari24 |
97.33 |
| 6 |
mcg96 |
97.19 |
| 7 |
ignaciomasari24 |
97.14 |
| 8 |
ignaciomasari21 |
96.73 |
| 9 |
ignaciomasari21 |
96.70 |
| 10 |
ignaciomasari21 |
96.66 |
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 |
ignaciomasari18 |
90.51 |
| 10 |
mcg96 |
90.25 |
Avon12
| # |
Nickname |
Score [%] |
| 1 |
yxl7245 |
95.35 |
| 2 |
yxl7245 |
95.35 |
| 3 |
yilong_rit |
95.35 |
| 4 |
Nirmalan |
95.19 |
| 5 |
cpoteet1 |
95.11 |
| 6 |
cpoteet0 |
95.09 |
| 7 |
EonRehman |
95.08 |
| 8 |
EonRehman |
95.08 |
| 9 |
EonRehman |
95.08 |
| 10 |
cpoteet0 |
95.07 |
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.
