The downloadable files contain all data and associated scripts that generate results as seen in the article. The major component description and detailed setup and run instructions are also provided in the README file.


The project is conceptualized to 'Geo Web-Based Facility Mapping for Zone-2 in Greater Visakhapatnam Municipal Corporation- GVMC in Visakhapatnam, India'. The main objective is to share the spatial data to the public to help them find the information about the nearest Hospital, ATM, Educational institutions, petrol filling stations, and supermarkets by providing both web map services and web coverage services using QGIS Cloud.


Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.


The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macroscopic degree before arriving at the sensor element of the imaging spectrometer as mixed spectra.


 This data package is parepared by Dr. Jianguo Niu (IMSG at NOAA NESDIS/STAR) on

        March 18, 2020


 The purpose of this OMPS LFSO2 retrieval products package is in support the paper:

 "Evaluation and Improvement of the Near-real-time Linear Fit SO2 retrievals from Suomi NPP (S-NPP) Ozone Mapping & Profiler Suite"


This package includes LFSO2 V8TOS retrievals of:

        1. "logic swith on" (original set as described by th paper 01824) products


 This data are in NetCDF format. Which can be read by an IDL code "". The usage example




The "data" is a structure, which included most of the parameters you needed. 





Dataset described in: 

Daudt, R.C., Le Saux, B., Boulch, A. and Gousseau, Y., 2019. Multitask learning for large-scale semantic change detection. Computer Vision and Image Understanding, 187, p.102783.


This dataset contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided, generated by rasterizing Urban Atlas 2006, Urban Atlas 2012, and Urban Atlas Change 2006-2012 maps. 


The dataset is split into five parts:

    - 2006 images 



Please contact us if you have any questions.


Subpixel classification (SPC) extracts meaningful information on land-cover classes from the mixed pixels.However, the major challenges for SPC are to obtain reliable soft reference data (RD), use apt input data, and achieve maximum accuracy. This article addresses these issues and applies the support vector machine (SVM) to retrieve the subpixel estimates of glacier facies (GF) using high radiometric-resolution Advanced Wide Field Sensor (AWiFS) data. Precise quantification of GF has fundamental importance in the glaciological research.


The submitted file is a supplemental of IEEE JSTAR article with DOI: 10.1109/JSTARS.2019.2955955

The dataset consists of three sections. The first section briefly reviews the subpixel classification (SPC) techniques and justifies the use of support vector machines in this study. It also highlights the key contribution of this study in the field of glaciology.

The second section details the steps involved in correcting the geometric, atmospheric, and topographic effects in the satellite images. It also specifies about the conversion of thermal band data to surface temperature.

The third section indicates how the ancillary layers used in this study are helpful in the segregation of various glacier facies.

Besides this, three tables (A.1, A.2, and A.3) are given. Table A.1 lists the ancillary layers used in this study, their source and applicability. Table A.2 provides a brief review on the SPC of different land-covers. The reported accuracies were compared with those obtained in this study. Table A.3 quantitatively illustrates how the ancillary layers are able to distinguish among various glacier facies.       

The dataset also contains seven figures (Figs. A.1, A.2, A.3, A.4, A.5, A.6, and A.7) depicting the research approach, correlation between SPC-derived and reference glacier facies area, SPC outputs from eight-class case using spectral data, SPC outputs from three-class case using spectral data, SPC-derived and reference glacier facies area obtained for different cases, SPC accuracy statistics, and texture-based differentiation of glacier facies respectively.

Each of these sections, tables and figures have been referred in the main article at appropriate places.


Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.


=================  Authors  ===========================

Lichao Mou,

Yuansheng Hua,

Pu Jin,

Xiao Xiang Zhu,


=================  Citation  ===========================

If you use this dataset for your work, please use the following citation:


  title= {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

  author= {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

  journal= {IEEE Geoscience and Remote Sensing Magazine},

  year= {in press}



==================  Notice!  ===========================

This dataset is ONLY released for academic uses. Please do not further distribute the dataset on other public websites.


Dataset for change detection (before and after change) are generated by matlab code.


Hyperspectral data set includes Indian Pines  and   Salinas A