Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

This dataset is the model data required in the manuscript for IEEE Transactions on geoscience and remote sensing.

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The dataset contains hurricane Maria-induced outage duration at the barrio level derived from nighttime lights, along with the values of cofactors from socioeconomic and physical factors that influenced the recovery process. 

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This dataset  provide researchers a benchmark to develop applicable and adaptive harbor detection algorithms.

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Despite the growing knowledge on the significance of submarine groundwater discharge (SGD), mapping its occurrence is a continuing challenge.  This study explores the capability and applicability of low-cost, off-the-shelf, recreational-grade echosounders (RGES) to image different types and locate point sources of bubbly coastal SGD. Standard and systematic methodologies for efficient imaging and processing were established.

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<p>This is the image dataset for satellite image processing&nbsp; which is a collection therml infrared and multispectral images .</p>

Instructions: 

Dataset images
Thermal infrared images and multispectral images
image size:512x512
format:
image:.tiff
file :.h5

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In a context of rapid urban evolution, there is a need of surveying cities. Nowadays predictive models based on machine learning require large amount of data to be trained, hence the necessity of providing some public dataset allowing to follow up urban evolution. While most of changes occurs onto the vertical axis, there is no public change detection dataset composed of 3D point clouds and directly annotated according to the change at point level yet.

Instructions: 

Urban Point Clouds simulator

We have developed a simulator to generate time series of point clouds (PCs) for urban datasets. Given a 3D model of a city, the simulator allows us to introduce random changes in the model and generates a synthetic aerial LiDAR Survey (ALS) above the city. The 3D model is in practice issued from a real city, e.g. with a Level of Detail 2 (LoD2) precision. From this model, we extract each existing building as well as the ground. By adding or removing buildings in the model, we can simulate construction or demolition of buildings. Notice that depending on area the ground is not necessarily flat. The simulator allows us to obtain as many 3D PCs over urban changed areas as needed. It could be useful especially for deep learning supervised approaches that require lots of training dates. Moreover, the created PCs are all directly annotated by the simulator according to the changes, thus no time-consuming manual annotation needs to be done with this process.

For each obtained model, the ALS simulation is performed thanks to a flight plan and ray tracing with the Visualisation ToolKit (VTK) python library.  Space between flight lines is computed in accordance to predefined parameters such as resolution, covering between swaths and scanning angle. Following this computation, a flight plan is set with a random starting position and direction of flight in order to introduce more variability between two acquisitions. Moreover, Gaussian noise can be added to simulate errors and lack of precision in LiDAR range measuring and scan direction.

Dataset Description

To conduct fair qualitative and quantitative evaluation of PC change detection techniques, we have build some datasets based on LoD2 models of the first and second districts of Lyon  (https://geo.data.gouv.fr/datasets/0731989349742867f8e659b4d70b707612bece89), France. For each simulation, buildings have been added or removed to introduce changes in the model and to generate a large number of pairs of PCs. We also consider various initial states across simulations, and randomly update the set of buildings from the first date through random addition or deletion of  buildings to create the second landscape. In addition, flight starting position and direction are always set randomly. As a consequence, the acquisition patterns will not be the same between generated PCs, thus each acquisition may not have exactly the same visible or hidden parts.

From terrestrial LiDAR surveying to photogrammetric acquisition by satellite images, there exist many different types of sensors and acquisition pipelines to obtain 3D point clouds for urban areas, resulting in PCs with different characteristics. {By providing different acquisition parameters to our simulator}, our goal was to provide a variety of sub-datasets with heterogeneous qualities to reproduce the real variability of LiDAR sensors or to mimic datasets coming from a photogrammetric pipeline with satellite images (by using a tight scan angle with high noise). Thus, we generated the following sub-datasets:

  • ALS with low resolution, low noise for both dates
  • ALS with high resolution, low noise for both dates
  • ALS with low resolution, high noise for both dates
  • ALS with low resolution, high noise, tight scan angle (mimicking photogrammetric acquisition from satellite images) for both dates
  • Multi-sensor data, with low resolution, high noise at date 1, and high resolution, low~noise at date 2

Notice that sub-datasets 3 and 4 are quite similar but the latter provides less visible facades, thanks to the smaller scanning angle and overlapping percentage.

Finally, for the first configuration (ALS low resolution, low noise), we provided the 3~following different training sets:

  • Small training set: 1 simulation
  • Normal training set: 10 simulations
  • Large training set: 50 simulations

More details about the configuration of acquisition are provided in the documentation file and in the publication Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets, de Gélis et al. (2021)

Technical details

All PCs are available at PLY format. Each train, val, test folder contains sub-folders containing pairs of PCs : pointCloud0.ply and pointCloud1.ply for both first and second dates.

Each ply file contain the coordinates X Y Z of each points and the label:

  • 0 for unchanged points
  • 1 for points on a new building
  • 2 for for points on a destruction.

The label is given in a scalar field named label_ch. Notice that the first PC (pointCloud0.ply) has a label field even if it is set at 0 for every points because change are set in comparison to previous date.

Citation

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

@article{degelis2021change,  title={Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets},  author={{de G\'elis}, I. and Lef\`evre, S. and Corpetti, T. },  journal={Remote Sensing},  volume={13},  pages={2629},  year={2021},  publisher={Multidisciplinary Digital Publishing Institute}}

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The files here support the analysis presented in the Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021), published in IEEE Transactions on Geoscience and Remote Sensing [1]. In particular, we provide in the following the instructions to access the multilook interferogram sequences exploited in our Comment and their ancillary information.

 

This dataset contains modified Copernicus Sentinel data [2021].

 

Instructions: 

The overall dataset is composed of three multilook interferometric sequences retrieved by processing, through the P-SBAS processing chain described in [2], the 230 Sentinel-1 images acquired from ascending orbits (track 44) over Sicily (Southern Italy) between May 2016 and May 2020. 

The interferometric datasets, referred hereafter to as “short-time”, “medium-time” and “long-time”, respectively, are encapsulated within a different zip file. Instructions to manage the datasets are provided in the pdf attached file.

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The InSAR processing result,GPS result,gray model predicted result and Gray-Markov model predicted result 

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TBD

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