Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot
Abstract
:1. Background. Where Do We Come from?
- the larger the availability of GIS, easier software, the more affordable digital cartography became, which led to the creation of academic geomatics laboratories in several countries, and the creation of the Association of Geographic Information Laboratories for Europe (AGILE) in 1998.
- more and more RS data: SPOT-1 launched in 1986, synthetic aperture radar ERS-1 launched in 1991, as well as more new sensors, new wavelengths or new radar polarizations. These new devices required a full new range of processing methods, algorithms, format conversion procedures, forms of registration, structured recording, etc., The international research community was initiating large-scale experiments to specifically explore the fusion of multi-source geo-information. This was the time of several Hydrologic and Atmospheric Pilot Experiments HAPEX [7], and the Alpilles-ReSeDA (Remote Sensing Data Assimilation) European project [8].
2. Main Objectives: What’s at Stake?
2.1. Land Cover Assessment
- Urban and infrastructure expansion, between 1990 and 2012, which has affected land with productive soil and fragments the existing landscape structure.
- The EU’s agricultural land, often in favorable locations, has been decreasing at an average rate of 1000 km2 per year: Associated biodiversity and traditional landscapes are affected by land take, agricultural intensification and farmland abandonment, cf. Figure 3.
- The forest area remains stable, while indicating an intensification of forestland use. This may lead to a declining quality of forest ecosystems.
2.2. Disaster Response Management
3. Progress status: What’s Up? and What’s New?
3.1. Reviews and Literature Surveys on Specific Data Fusion Subtopics
- An IJGI special issue on “GEOBIA in a Changing World” (14 papers in 2018) [16], investigating how GEographic Object Based Image Analysis can complement pixel-based analysis. Basically, GEOBIA works with both original pixels and an overlaid areal or choropleth map, i.e., a vector representation automatically computed by a segmentation algorithm. This approach is particularly praised in land-cover studies, which are, by nature, object-based (crop fields, built-up areas, etc.), for instance: mapping rooftop vegetation [17].
- A Remote Sensing special issue on “Multi-Sensor and Multi-Data Integration in Remote Sensing” (21 papers in 2017) [18];
- A 2018 literature survey on Spatiotemporal Fusion of Multisource RS data [19];
- A 2019 review about Spatiotemporal Image Fusion in Remote Sensing [20];
- About Disaster Response Management, and Risk Assessment, an IJGI review article [23];
- About volunteered geo-info and crowdsourcing: a study on data quality assurance [24];
- And many, many … more!
3.2. What Does This Special Issue Bring to the Reader?
- Random Forest Regression [29]: applied individually to a RGB multispectral image and to a SAR image pre-processed by gray-level co-occurrence matrix descriptors (e.g., energy, homogeneity, angular second moment), to train independent classes by K-means, then training samples are selected and used in the learning process, determining the RF for each class, then applying the resulting RF to the entire image. This results in the fusion of thesurface roughness characteristics of the SAR image and the spectral characteristics of the MS image;
- Two-Branch Convolutional Neural Network [30]: CNN layers are applied to the respective input features: a principal component analysis (PCA) is performed on an hyper-spectral 144-bands image (branch 1—image provided at the IEEE-GRSS 2013 data fusion contest), then, in next layers, a series of filters whose parameters are tuned by supervised learning. The LiDAR image is processed in a similar manner (branch 2). A residual block is utilized in each branch to extract multi-scale features. Then, a fusion module is used to integrate HSI and LiDAR features, based on “squeeze-and-excitation networks” (adaptive adjust of the weighs).
- Multi-Source Time Series [31]: processing Landsat-7 ETM and Huanjing-1, over 13 years, with K-means classification, then analyzing the resulting time series with a combination of Mann-Kendall trend detection method and Theil-Sen slope analysis, to monitor the evolution of the NDVI on forest land.
- Similarity scores for Road-matching [32]: Hausdorff distance (between vertices), orientation (octant), sinuosity, mean perpendicular distance, mean length of the edges of a triangulated irregular network (TIN), and degree of connectivity (valence of intersections), are six indices used to compute a similarity score. The sources are the Istanbul Metropolitan authority, OSM, TomTom and Basarsoft (both of the latter are private navigation data).
- Point-to-Grid Distances for Point Clouds [33]: an improvement of the Iterative Closest Point (ICP) algorithm, including point-to-DEM pixel estimation, thus allowing the fusion of a point cloud (LiDAR flight campaign Apr.2017) with the digital elevation model computed from the LiDAR flight campaign Dec.2012 with a lower sampling interval (0.5 versus 0.1m.).
- 3D Reconstruction [34]: prismatic 3D building models are built by adding height values to a 2D footprint (source: OSM). The heights are computed with two different approaches, depending on the availability of sources providing a sufficient high-resolution spatial coverage at an urban scale. Sources are: (1) a pair of independent elevation models (CartoSat-1 and TanDEM-X), whose fusion results into a more accurate reference DEM;(2) a pair of a SAR and an optical image (TerraSAR-X and WorldView-2), processed by stereo grammetry fusion yielding to a DEM. High-resolution airborne LiDAR point cloud data are available only at some sub-areas, and serve as an accuracy assessment.
4. New Needs, New Challenges, New Issues
4.1. Geo-Information Confronted to the Big Data Paradigm
- new RS satellites: the European Sentinel-2 [36], and Earth Observation satellites from many new countries in the last decade, e.g., Dubai, Indonesia, Nigeria, and Venezuela, to cite a few;
- new sensors, passive or active, that are bringing more repetitive coverage, are more spatially accurate, and have a wider spectral range;
- and the forthcoming spreading of IoT sensors, whose fusion with other geo-information data sources starts being studied recently [41]. In the marine domain, Fish Aggregating Devices (FAD’s), possibly remotely monitored, are a rare source of “ground” information for oceans [42], whose development is growing very fast (though under almost no control).
- the CEOS “virtual constellations”: an inter-Agency collaboration to coordinating space-based, and ground-based data delivery systems to meet a common set of requirements within specific domains: Atmospheric Composition, Land Surface Imaging, Precipitations, and Ocean (sea surface topography and temperature, color, wind). Regarding Land Surface Imaging, the “Analysis Ready Data for Land” are satellite data processed to a minimum set of requirements, and organized into an interoperable form that allows immediate analysis with a minimum of additional user effort [43];
- the “data cube” solution: an online analytical processing (OLAP) software, to piling up data layers, over a 2D geographic grid, making cubes with many dimensions [44].
4.2. Geo-Information to the Industry, and Back
“The challenge of detecting and tracking multiple objects of interest, using multiple disparate sensors, can generally be broken down into the following steps:
- -
aligning the data from different sensors from both a spatial and temporal perspective- -
associating the data recorded by disparate sources against the same object of interest- -
mathematically fusing the different pieces of data/information.”
5. Conclusions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
CEOS | Committee on Earth Observations Satellites (inter-governmental body) |
DEM | Digital Elevation Model |
GIS | Geographic Information System |
GSM | Global System for Mobiles |
HAPEX | Hydrologic and Atmospheric Pilot Experiments |
OSM | OpenStreetMap (open cartographic data, open wiki platform) |
HOT | Humanitarian OpenStreetMap Team (US academic based) |
IoT | Internet of Things |
NDVI | Normalized Difference Vegetation Index |
RS | Remote Sensing |
RS-15’s | the 15 glorious years of Remote Sensing (1972–87) |
SAR | Synthetic Aperture Radar |
UAV | Unmanned Aerial Vehicle |
VGI | Volunteered Geographic Information |
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Jeansoulin, R. Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot. ISPRS Int. J. Geo-Inf. 2019, 8, 330. https://doi.org/10.3390/ijgi8080330
Jeansoulin R. Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot. ISPRS International Journal of Geo-Information. 2019; 8(8):330. https://doi.org/10.3390/ijgi8080330
Chicago/Turabian StyleJeansoulin, Robert. 2019. "Multi-Source Geo-Information Fusion in Transition: A Summer 2019 Snapshot" ISPRS International Journal of Geo-Information 8, no. 8: 330. https://doi.org/10.3390/ijgi8080330