The data  in journal "The Investigation and Analysis of Lophodermium Piceae in Mountainous Areas Based on Multi-Spectral Remote Sensing Imagery".


Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 


Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware.


The top-level of this data-set has two folders corresponding to the two algorithms studied (exploration and task allocation). Each folder contains a simulation and field deployment sub-folder, which in turn contains the data files. In the simulation folder, there are additional sub-folders labeled based on the experimental configuration used during the experiment. In particular, the simulation folders are labeled as PacketDropRate_NumberOfRobots_RepetitionNumber (for example, a folder with 0.0_6_1 denotes an experimental setting with a packet drop of 0.0, 6 robots and the first repetition). Each simulation folder for the exploration algorithm contains a ROS bag file, a CSV file (one for each robot, containing the Voronoi tessellations as computed by the robot), a log file (one for each robot, containing the logs generated by ROSBuzz) and a folder containing the CSV files generated from the ROS topics using the corresponding bag file. The field deployment sub-folders for the task allocation algorithm contain six experimental trials using field robots. Each of these experimental folders contains the ROSBuzz log files generated by the field robots, one for each robot. Similarly, the field deployment sub-folder within the Semi-autonomous exploration folder contains the CSV files generated from the ROS bag files during the three field deployment experiments. Only the CSV files used to generate the trajectories for the user-centric study (exploration) are provided in this data-set to keep the anonymity of the user.   The python notebooks used to parse the data provided in this data-set and generate the plots are also attached.


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.


 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. 



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. 


High Resolution Semantic Change Detection (HRSCD) Dataset




Rodrigo Caye Daudt,

Bertrand Le Saux,

Alexandre Boulch,

Yann Gousseau,





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 

    - 2012 images

    - Change labels

    - 2006 land cover maps

    - 2012 land cover maps


The 2006 images are under a non-redistributable licence, but can be downloaded directly from IGN's website ( or The necessary files are:

    - BD ORTHO® 50 cm, D 14 - CALVADOS, 2005

    - BD ORTHO® 50 cm, D 35 - ILLE-ET-VILLAINE, 2006

For help with downloading the images please contact the authors.





Change labels are available for all image pairs in the dataset. Available classes:

    - 0: No change

    - 1: Change


Land cover maps are available for all images in the dataset. Urban Atlas classes have been grouped at first hierarchical level, as described in the paper cited above. Available classes:

    - 0: No information

    - 1: Artificial surfaces

    - 2: Agricultural areas

    - 3: Forests

    - 4: Wetlands

    - 5: Water






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



title = "Multitask Learning for Large-scale Semantic Change Detection",

journal = "Computer Vision and Image Understanding",

volume = "187",

pages = "102783",

year = "2019",

issn = "1077-3142",

doi = "",

url = "",

author = "Daudt, {Rodrigo Caye} and {Le Saux}, Bertrand and Boulch, Alexandre and Gousseau, Yann",

keywords = "Semantic change detection, High resolution Earth observation, Fully convolutional networks, Remote sensing, Multitask learning"






The images in this dataset are released under IGN's "licence ouverte". More information can be found at


The maps used to generate the labels in this dataset come from the Copernicus program, and as such are subject to the terms described here:


This dataset is released under Creative-Commons BY-NC-SA licence. For commercial purposes, please contact the authors.


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.


Beijing Building Dataset(BGB) is an elevation satellite image dataset which is integrated by satellite image and aerial photograph for building detection and identification. It contains 2000 images from Google Earth History Map of five different areas in Beijing on November 24th, 2016, and all these images are 512*512 in resolution ratio with a precision of 0.458m. It covers more than 100 km2 geographic areas of Beijing both in suburbs and urban areas.


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