Web Coverage Processing Service Research Articles

A Scalable Geospatial Web Service for Near Real-Time, High-Resolution Land Cover Mapping

A land cover classification service is introduced toward addressing current challenges on the handling and online processing of big remote sensing data. The geospatial web service has been designed, developed, and evaluated toward the efficient and automated classification of satellite imagery and the production of high-resolution land cover maps. The core of our platform consists of the Rasdaman array database management system for raster data storage and the open geospatial consortium web coverage processing service for data querying. Currently, the system is fully covering Greece with Landsat 8 multispectral imagery, from the beginning of its operational orbit. Datasets are stored and preprocessed automatically. A two-stage automated classification procedure was developed which is based on a statistical learning model and a multiclass support vector machine classifier, integrating advanced remote sensing and computer vision tools like Orfeo Toolbox and OpenCV. The framework has been trained to classify pansharpened images at 15-m ground resolution toward the initial detection of 31 spectral classes. The final product of our system is delivering, after a postclassification and merging procedure, multitemporal land cover maps with 10 land cover classes. The performed intensive quantitative evaluation has indicated an overall classification accuracy above 80%. The system in its current alpha release, once receiving a request from the client, can process and deliver land cover maps, for a 500- $\text{km}^2$ region, in about 20 s, allowing near real-time applications.

A Distributed Infrastructure for Earth-Science Big Data Retrieval

Earth-Science data are composite, multi-dimensional and of significant size, and as such, continue to pose a number of ongoing problems regarding their management. With new and diverse information sources emerging as well as rates of generated data continuously increasing, a persistent challenge becomes more pressing: To make the information existing in multiple heterogeneous resources readily available. The widespread use of the XML data-exchange format has enabled the rapid accumulation of semi-structured metadata for Earth-Science data. In this paper, we exploit this popular use of XML and present the means for querying metadata emanating from multiple sources in a succinct and effective way. Thereby, we release the user from the very tedious and time consuming task of examining individual XML descriptions one by one. Our approach, termed Meta-Array Data Search (MAD Search), brings together diverse data sources while enhancing the user-friendliness of the underlying information sources. We gather metadata using different standards and construct an amalgamated service with the help of tools that discover and harvest such metadata; this service facilitates the end-user by offering easy and timely access to all metadata. The main contribution of our work is a novel query language termed xWCPS, that builds on top of two widely-adopted standards: XQuery and the Web Coverage Processing Service (WCPS). xWCPS furnishes a rich set of features regarding the way scientific data can be queried with. Our proposed unified language allows for requesting metadata while also giving processing directives. Consequently, the xWCPS-enabled MAD Search helps in both retrieval and processing of large data sets hosted in an heterogeneous infrastructure. We demonstrate the effectiveness of our approach through diverse use-cases that provide insights into the syntactic power and overall expressiveness of xWCPS. We evaluate MAD Search in a distributed environment that comprises five high-volume array-databases whose sizes range between 20 and 100 GB and so, we ascertain the applicability and potential of our proposal.

PlanetServer: Innovative approaches for the online analysis of hyperspectral satellite data from Mars

PlanetServer is a WebGIS system, currently under development, enabling the online analysis of Compact Reconnaissance Imaging Spectrometer (CRISM) hyperspectral data from Mars. It is part of the EarthServer project which builds infrastructure for online access and analysis of huge Earth Science datasets. Core functionality consists of the rasdaman Array Database Management System (DBMS) for storage, and the Open Geospatial Consortium (OGC) Web Coverage Processing Service (WCPS) for data querying. Various WCPS queries have been designed to access spatial and spectral subsets of the CRISM data. The client WebGIS, consisting mainly of the OpenLayers javascript library, uses these queries to enable online spatial and spectral analysis. Currently the PlanetServer demonstration consists of two CRISM Full Resolution Target (FRT) observations, surrounding the NASA Curiosity rover landing site. A detailed analysis of one of these observations is performed in the Case Study section. The current PlanetServer functionality is described step by step, and is tested by focusing on detecting mineralogical evidence described in earlier Gale crater studies. Both the PlanetServer methodology and its possible use for mineralogical studies will be further discussed. Future work includes batch ingestion of CRISM data and further development of the WebGIS and analysis tools.

Cloud Implementation of a Full Hyperspectral Unmixing Chain Within the NASA Web Coverage Processing Service for EO-1

The launch of the NASA Earth Observing 1 (EO-1) platform in November 2000 marked the establishment of spaceborne hyperspectral technology for land imaging. The Hyperion sensor onboard EO-1 operates in the 0.4-2.5 micrometer spectral range, with 10 nanometer spectral resolution and 30-meter spatial resolution. Spectral unmixing has been one of the most successful approaches to analyze Hyperion data since its launch. It estimates the abundance of spectrally pure constituents (endmembers) in each observation collected by the sensor. Due to the high spectral dimensionality of Hyperion data, unmixing is a very time-consuming operation. In this paper, we develop a cloud implementation of a full hyperspectral unmixing chain made up of the following steps: 1) dimensionality reduction; 2) automatic endmember identification; and 3) fully constrained abundance estimation. The unmixing chain will be available online within the Web Coverage Processing Service (WCPS), an image processing framework that can run on the cloud, as part of the NASA SensorWeb suite of web services. The proposed implementation has been demonstrated using the EO-1 Hyperion imagery. Our experimental results with a hyperspectral scene collected over the Okavango Basin in Botswana suggest the (present and future) potential of spectral unmixing for improved exploitation of spaceborne hyperspectral data. The integration of the unmixing chain in the WCPS framework as part of the NASA SensorWeb suite of web services is just the start of an international collaboration in which many more processing algorithms will be made available to the community through this service. This paper is not so much focused on the theory and results of unmixing (widely demonstrated in other contributions) but about the process and added value of the proposed contribution for ground processing on the cloud and onboard migration of those algorithms to support the generation of low-latency products for new airborne/spaceborne missions.

The OGC web coverage processing service (WCPS) standard

Imagery is more and more becoming integral part of geo services. More generally, an increasing variety of sensors is generating massive amounts of data whose quantized nature frequently leads to rasterized data structures. Examples include 1-D time series, 2-D imagery, 3-D image time series and x/y/z spatial cubes, and 4-D x/y/z/t spatio-temporal cubes. The massive proliferation of such raster data through a rapidly growing number of services make open, standardized service interfaces increasingly important. Geo service standardization is undertaken by the Open GeoSpatial Consortium (OGC). The core raster service standard is the Web Coverage Service (WCS) which specifies retrieval based on subsetting, scaling, and reprojection. In 2008, OGC has issued a companion standard which adds flexible, open-ended coverage processing capabilities. This Web Coverage Processing Service (WCPS) specifies a coverage processing language allowing clients to send requests of arbitrary complexity for evaluation by the server. This contribution reports on the WCPS standard by giving an introduction to its coverage model and processing language. Further, design rationales are discussed, as well as background and relation to other OGC standards. 1-D to 4-D use case scenarios illustrate intended use and benefits for different communities. Although the paper focuses on conceptual issues, the WCPS reference implementation, PetaScope, is briefly addressed. The author is co-chair of the coverage-related working groups in OGC.

Language-Based Access to Large Sensor Repositories

Sensor data have broadened their scope recently, ranging now from the simple time series measurements to, e.g., hyperspectral satellite image maps timeseries. In addition to observed data, simulation data increasingly have to be merged, for example 4-D ocean and atmospheric data. The majority of these data fall into the category of multi-dimensional rasters. However, when it comes to flexible retrieval, including sensor data search, aggregation, analysis, fusion, etc., standard query language support in the past has not kept up with the service level of, e.g., metadata retrieval. To close this gap, the Open GeoSpatial Consortium (OGC) has issued the Web Coverage Processing Service (WCPS) Standard in December 2008. WCPS defines a request language for multi-dimensional raster data, suitable for specifying navigation, download, and analysis of sensor, image, and statistics data. This contribution emphasises sensor data modeling and the perspectives for an integrated, cross-dimensional sensor data retrieval. Further, the WCPS reference implementation is briefly discussed.