Publication Date: 2020-01-08
Approval Date: 2019-11-22
Submission Date: 2019-08-22
Reference number of this document: OGC 19-046r1
Reference URL for this document: http://www.opengis.net/doc/PER/t15-D023
Category: OGC Public Engineering Report
Editor: Scott Serich
Title: OGC Testbed-15: Quebec Model MapML Engineering Report
COPYRIGHT
Copyright © 2020 Open Geospatial Consortium. To obtain additional rights of use, visit http://www.opengeospatial.org/
WARNING
This document is not an OGC Standard. This document is an OGC Public Engineering Report created as a deliverable in an OGC Interoperability Initiative and is not an official position of the OGC membership. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Further, any OGC Public Engineering Report should not be referenced as required or mandatory technology in procurements. However, the discussions in this document could very well lead to the definition of an OGC Standard.
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- 1. Subject
- 2. Executive Summary
- 3. References
- 4. Terms and definitions
- 5. Overview
- 6. Background
- 7. Québec Model MapML Service
- 8. Quebec model MapML Client
- Appendix A: Revision History
1. Subject
This OGC Testbed-15 Engineering Report (ER) describes the Map Markup Language (MapML) enabled client component implementation for the Quebec Lake-River Differentiation Model in the Machine Learning (ML) task of Open Geospatial Consortium (OGC) Testbed-15 (T-15). This ER presents the MapML parsing capabilities that were developed to illustrate the outputs of a ML model to delineate lake and river features from an undifferentiated waterbody vector dataset in Québec, Canada. Client data was accessed through an OGC Web Processing Service (WPS) interface in coordination with an OGC API - Features implementation.
2. Executive Summary
The Testbed-15 (T-15) Map Markup Language (MapML) work built on the momentum documented in the OGC Testbed-14 MapML Engineering Report. The T-15 ER includes a description of the MapML-enabled client component implementation for the Quebec Lake-River Differentiation machine-learning (ML) task. Client parsing capabilities enabled display of model outputs that delineated lake and river features from undifferentiated waterbodies.
MapML is a text format for encoding map information for the World Wide Web. The objective of MapML is to allow Web-based user agent software (browsers and others) to display and edit maps and map data without necessary customization.
MapML is unique relative to other maps on the web encodings but there is also some duplication. What makes MapML unique is that it takes Spatial Data on the Web Best Practices and applies them for the direct benefit of HyperText Markup Language (HTML) users (as contrasted with web developers).
As described by the MapML use cases and requirements. MapML is an extension to HTML. If implemented, this implies that the browser understands map/layer semantics (however those elements are eventually named) as well as feature, property, or geometry semantics. MapML is intended to be user-oriented. This includes enabling users to create web pages in all manner of styles while having a solid foundation in HTML and Cascading Style Sheets (CSS) as well as JavaScript for progressive enhancement. Today, there is no built-in map/layer behavior in web browsers, nor is there feature/property/geometry semantics. MapML provides the ability to encode map/layer and feature/property/geometry semantics in a single format that will be read and interpreted by web browsers directly. MapML brings geographic information to the web browser, thereby making the web browser the user agent.
MapML maintains a different focus as compared to other geographic encodings such as GeoJSON, Geography Markup Language (GML), and Keyhole Markup Language (KML). These encodings require interpretation and/or processing and are not native in web browsers. MapML should not be constrained by other encodings if the user requires additional capabilities such as markup in coordinate strings and possibly other requirements. A major objective of MapML is to make the browser "understand" not only where the user is but also to understand where features are in relation to the user.
2.1. Document contributor contact points
All questions regarding this document should be directed to the editor or contributors:
Contacts
Name | Organization | Role |
---|---|---|
Scott Serich |
Open Geospatial Consortium |
Editor |
Gil Heo |
George Mason University |
Contributor |
Jérôme Jacovella-St-Louis |
Ecere Corporation |
Contributor |
Peter Rushforth |
Natural Resources Canada |
Contributor |
2.2. Foreword
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.
Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation.
3. References
The following normative documents are referenced in this ER:
4. Terms and definitions
For the purpose of this report, the definitions specified in Clause 4 of the OGC Web Services (OWS) Common Implementation Standard OGC 06-121r9 shall apply. In addition, the following terms and definitions apply.
- ● Dnnn
-
Deliverable IDs used during the initiative to uniquely identify various components, engineering reports, etc. (for example, the D106 Quebec Model MapML Client component).
- ● map
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portrayal of geographic information as a digital image file suitable for display on a computer screen
- ● portrayal
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presentation of information to humans (source: ISO 19117)
4.1. Abbreviated terms
-
API - Application Programming Interface
-
CORS - Cross-Origin Resource Sharing
-
CSS - Cascading Style Sheets
-
DOM - Document Object Model
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EC2 - Elastic Compute Cloud
-
GML - Geography Markup Language
-
HTML - Hypertext Markup Language
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IANA - Internet Assigned Numbers Authority
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ISO - International Organization for Standardization
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JPEG - Joint Photographic Experts Group
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JSON - JavaScript Object Notation
-
KML - Keyhole Markup Language
-
MapML - Map Markup Language
-
MVC - Model-View-Controller
-
OGC - Open Geospatial Consortium
-
OWS - OGC Web Services
-
PNG - Portable Network Graphics
-
SVG - Scalable Vector Graphics
-
URL - Uniform Resource Locator
-
WFS - Web Feature Service
-
WMS - Web Map Service
-
WMTS - Web Map Tile Service
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WPS - Web Processing Service
5. Overview
Much of the OGC Testbed-15 Quebec Model MapML work was based on foundational work of the MapML W3C Community Group and the OGC Testbed-14 MapML work documented in the Testbed-14 Engineering Report.
Section 5 of this ER describes the background of the current work.
Section 6a describes the OGC API - Features implementation that served MapML data to the client.
Section 6b describes the Quebec MapML Client that rendered retrieved features to a browser user.
6. Background
The overall architecture of the Testbed-15 Machine Learning Task was based on a broad set of scenarios ranging from image classification to dataset discovery. The objective of the Quebec Lake-River Differentiation Model subtask was to create and deploy a machine-learning model to differentiate between rivers and lakes from otherwise unlabeled bodies of water. The main focus of the work was to provide a service to determine whether a body of water should be split into a lake and a river, and to provide identified and labeled outputs when such splits occur. When no split was required, each body of water was labeled as either lake or river. The procedure for applying the model was to:
-
Recommend whether a water body should be split into lake and river features.
-
Evaluate the confidence level of a recommendation.
-
Apply the recommendation to the dataset.
-
Test and correct the resultant dataset for topological and cartographical issues.
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Serve the data on an OGC API - Features interface using a MapML encoding.
Each service component was fronted by the relevant OGC interface, WPS for workflow management and OGC API - Features for features.
7. Québec Model MapML Service
In support of the Testbed-15 Québec Lakes/Rivers experiments, Ecere was responsible for the development of a service component to facilitate access to the potentially large volumes of vector data produced by running the differentiation Machine Learning model.
7.1. Service description
Ecere developed and deployed a new MapML-enabled OGC API end-point based on its newest iteration of the GNOSIS Map Server and Software Development Kit.
The endpoint presents all supported OGC API capabilities as parts of a single resource tree.
The modular OGC API building blocks implemented for Testbed-15 include:
-
(Vector) Features
-
Tiles
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Styles (retrieval only)
The service currently offers the data in the EPSG:4326 (plate carree, WGS84 spheroid) coordinate reference system (CRS). Using the draft OGC Tiles API specification, the data is provided in the tiling scheme’s CRS, except for vector data formats mandating EPSG:4326 (e.g. GeoJSON). A number of tiling schemes based on both spherical Mercator and EPSG:4326 are currently supported.
The data is available in different data formats:
-
GeoJSON (vector)
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Mapbox Vector Tiles (vector)
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GNOSIS Map Tiles (vector, imagery, coverage — tiles only)
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16-bit PNG (coverage with a specific range intended to accommodate digital elevation models, until the service offers GeoTIFF support)
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PNG and JPEG (imagery)
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MapML (vector, support for which was implemented during Testbed-15 and was the focus of this activity)
Support for GML and GeoECON, available in earlier iteration of the GNOSIS Map Server, will also be re-enabled at the endpoint presented above.
7.2. Data layers served
A number of data layers are accessible from this service, including data from Natural Earth, NASA BlueMarble (next generation), and regional sections of OpenStreetMap.
The data layers specific to the Testbed’s Québec Lakes/Rivers Machine Learning activities are organized within group of collections named GRHQ. GRHQ stands for Géobase du réseau hydrographique du Québec, free and open hydrography data covering the entire Province of Québec. These data are provided by the Ministère de l’Énergie et des Ressources naturelles (MERN). Together with High Resolution Digital Elevation Models, this was the source hydrography data set used for both training and testing the machine learning model.
Originally, only the output data from the D104 - Quebec Lakes/Rivers Machine Learning Model component was intended to be served. However due to constraints delaying the availability of that output data, the source hydrography vector data was provided instead.