OGC Engineering Report

OGC Disaster Pilot: User Readiness Guide
Andrew Lavender Editor Dr Samantha Lavender Editor
OGC Engineering Report


Document number:21-075
Document type:OGC Engineering Report
Document subtype:
Document stage:Published
Document language:English

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I.  Abstract

Improving the ability of key disaster decision makers and responders to discover, manage, access, transform, share, and exploit location-based and Earth Observation data will enhance decision making and, hopefully, save lives. The OGC Disaster Pilot 2021 has developed a number of prototype capabilities to demonstrate solutions for providing consistent, and reliable information to enable real-time actions to be taken using multiple technologies working together through pre-agreed standards.

This User Guide describes how the solution works, how users can be part of it, and showcases what can be achieved if everyone is willing to work together and share data and knowledge to improve the information available to those responding to a disaster.

II.  Executive Summary

Improving the ability of key disaster decision makers and responders to discover, manage, access, transform, share, and exploit location-based and Earth Observation (EO) information will enhance decision making and save lives. The full ambition of the OGC Disaster Pilot 2021 (termed Pilot) is to have such data available within the ‘golden hour’ of the disaster – the first sixty minutes — which is the key time for affecting the future outcomes of the overall response.

To deliver on such an ambition requires the use of multiple technologies underpinned by pre-agreed standards that would establish a robust solution with no single point of weakness, and enable a rapid deployment when a disaster occurs.

The Pilot has focused on developing a number of prototypes to demonstrate how a solution could be established to help users find disaster-relevant data with a particular focus on EO data, process it to develop analysis ready datasets that are easily sharable, use these datasets to create decision ready indicators to improve the amount and speed of data-driven decisions, and to provide tools to visualize, communicate and collaborate with everyone involved in the disaster response.

The Pilot has used three Case Studies to demonstrate what is possible, each with a different disaster focus, these are:

This User Guide sets out the history of using location-based geospatial data in disaster response and the future vision for how a solution would work. It also highlights the importance of being ready to participate in such a solution, it defines what readiness means, sets out the steps users need to undertake to achieve readiness and defines the pre-agreed standards that need to be implemented. Finally, it showcases the capabilities that the Pilot has achieved and the future recommendations and next steps to take this forward.

This Pilot is just the start and there is so much more that can be achieved if everyone is willing to work together and share data and knowledge to improve the information available to those responding to a disaster.

III.  Keywords

The following are keywords to be used by search engines and document catalogues.

Disasters, Natural Hazards, Landslides, Health, SDI, Analysis Ready Data, ARD, Decision Ready Information, Flood, Indicators, Emergency Response, Health, Pandemic, ogcdoc, OGC document, DP21, User Readiness Guide

IV.  Preface

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.

V.  Security considerations

No security considerations have been made for this document.

VI.  Submitting Organizations

The following organizations submitted this Document to the Open Geospatial Consortium (OGC):

VII.  Submitters

All questions regarding this document should be directed to the editor or the contributors:

Name Organization Role
Andrew Lavender Pixalytics Ltd Editor
Dr Samantha Lavender Pixalytics Ltd Editor
Antonio San José Satellogic Editor
Ryan Ahola Natural Resources Canada Contributor
Omar Barrilero European Union Satellite Centre Contributor
Dave Borges NASA Contributor
Paul Churchyard Contributor
Ignacio Nacho Correas Skymantics Contributor
Theo Goetemann GISMO Contributor
Ajay K Gupta Contributor
Dean Hintz Safe Software Contributor
Amy Jeu GISMO Contributor
Dave Jones StormCenter Communications Contributor
Albert Kettner Consultant Contributor
Alan Leidner Consultant Contributor
Adrian Luna European Union Satellite Centre Contributor
Niall McCarthy Crust Tech Contributor
Carl Reed Carl Reed and Associates Contributor
Guy Schumann RSS-Hydro Contributor
Marie-Françoise Voidrot Open Geospatial Consortium Contributor
Jiin Wenburns GISMO Contributor
Peng Yue Wuhan University Contributor

OGC Disaster Pilot: User Readiness Guide

1.  Scope

This User Guide describes how the solution works, how users can be part of it, and showcases what can be achieved if everyone is willing to work together and share data and knowledge to improve the information available to those responding to a disaster.

2.  Terms, definitions and abbreviated terms

This document uses the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this document and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.

This document also uses terms defined in the OGC Standard for Modular specifications (OGC 08-131r3), also known as the ‘ModSpec’. The definitions of terms such as standard, specification, requirement, and conformance test are provided in the ModSpec.

For the purposes of this document, the following additional terms and definitions apply.

2.1.  Terms and definitions

2.1.1. ARD

Analysis Ready Data and datasets — This is raw data that have had some initial processing created in a format that can be immediately integrated with other information and used within a Geographical Information System (GIS).

2.1.2. CRS

Coordinate Reference System — coordinate system that is related to the real world by a datum term name (source: ISO 19111)

2.1.3. DRI

Decision Ready Information and indicators — ARDs that have undergone further processing to create information and knowledge in a format that provides specific support for actions and decisions that have to be made about the disaster.

2.1.4. Indicator

Indicator —  An indicator is a realistic and measurable criteria.

2.1.5. Lidar

Light detection and ranging — a common method for acquiring point clouds through aerial, terrestrial, and mobile acquisition methods.

2.1.6. GeoNode

GeoNode — a web-based platform for deploying a GIS.

2.1.7. GeoPackage

GeoPackage — an open, standards-based, compact format for transferring geospatial information.

2.1.8. GeoServer

GeoServer —  GeoServer is a Java-based server that allows users to view and edit geospatial data. Using open standards set forth by the Open Geospatial Consortium (OGC), GeoServer allows for great flexibility in map creation and data sharing..

2.1.9. JSON-LD

JavaScript Object Notation — Linked Data — a lightweight linked data format based on JSON.

2.1.10. Radar

Radio detection and ranging — a detection system that uses radio waves to determine the distance (range), angle, or velocity of objects.

2.1.11. SAR

Synthetic Aperture Radar — a type of active data collection where a sensor produces its own energy and then records the amount of that energy reflected back after interacting with the Earth.

2.2.  Abbreviated terms


Copernicus Atmosphere Monitoring Service


Committee on Earth Observation Satellites


French Space Agency


Cloud Optimized GeoTIFF


National Commission for Aerospace Research and Development’s, Peru


Centre for Research on the Epidemiology of Disasters


Canadian Space Agency


Digital Elevation Model


Natural Resources Canada’s Emergency Geomatics Service


Copernicus Emergency Management Service


Earth Observation


Natural Resources Canada’s Earth Observation Data Management System


European Space Agency


Earth Science Information Partners


Federal Emergency Management Agency


Geographic Information Systems


New York City Geospatial Information Systems & Mapping Organization


High Resolution Data


Infrastructure for Spatial Information in the European Community


Integration Ready Data


Japan Aerospace Exploration Agency


JavaScript Object Notation — Linked Data


National Aeronautics and Space Administration, US


National Centers for Environmental Information, US


National Oceanic & Atmospheric Administration, US


Natural Resources Canada


Near Real-Time


Open Geospatial Consortium


Operational Readiness Levels


Synthetic Aperture Radar


Spatial Data Infrastructure


Socioeconomic Data Applications Center


Sea Surface Temperature


SpatioTemporal Asset Catalog


US Geological Survey


Web Coverage Service


Web Feature Service


World Health Organization


World Meteorological Organization


Web Map Tile Service

3.  Introduction

For over 20 years the Open Geospatial Consortium (OGC) has been working on the challenges of information sharing for emergency and disaster management, including response. The goal of the OGC Disaster Pilot 2021 (Pilot) was to look at fast moving scenarios where the rapid sharing of interoperable data requiring minimum preparation to use can provide disaster response teams with geospatial information that makes a real difference to the response activities.

The Pilot tested prototyping for the use of geospatial data in disaster response. To demonstrate the potential, the Pilot team focused on implementing data sharing for a small number of scenarios in a handful of regions. These were:

This User Readiness Guide aims to provide potential users with an introduction for rapidly using geospatial information in a disaster situation. This goal is accomplished by providing a future framework for how data providers will provide data enabled by standards to allow users to more quickly analyze, integrate and visualize such data to help make decisions and take actions. This report is a non-technical description of the work undertaken in the project. The report details a case study for each of the three chosen scenarios. The report concludes with a discussion of next steps to implement this framework more fully. A more detailed technical description can be found in the accompanying Provider Readiness Guide (OGC 21-074) [1].

3.1.  Disasters

Although there are varying definitions as to what constitutes a disaster event, the general consensus is that the number of these disaster events are increasing over time. In September 2021, the World Meteorological Organization (WMO) released ‘The Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019)’ calculated using data from the Centre for Research on the Epidemiology of Disasters (CRED) [7]. The report describes that over the last 50 years, 50% of all recorded disasters, 45% of related deaths and 74% of related economic losses were due to weather, climate and water related events, translating to 2.06 million deaths, and US$ 3.6 trillion in economic losses [8]. In addition to CRED, the World Health Organization (WHO), Public Health England, and the United Nations Office for Disaster Risk Reduction (UNDRR) also contributed to the report.

The report also indicates that the number of disasters has increased by a factor of five over the 50 year period, although they acknowledge that this is partly due to improved reporting. However, the WMO also noted that the number of weather, climate, and water extremes are increasing and will become more frequent and severe in many parts of the world as a result of climate change.

Looking specifically at 2020, excluding the COVID-19 pandemic, there were 389 disaster events recorded by CRED. These events resulted in 15,080 deaths, impacting 98.4 million people, and creating economic losses of at least US$ 171.3 billion. During the same period, the COVID-19 pandemic resulted in almost two million deaths, 90 million confirmed cases and impacted many more, creating trillions of dollars of economic losses.

CRED, who have maintained a database of these disaster events for over 30 years, and define a disaster as ‘a situation or event that overwhelms local capacity, necessitating a request at national or international level for external assistance; an unforeseen event that causes great damage, destruction and human suffering.’ The figures for 2020 were higher than the average number for the previous decade, which was 368, although slightly lower than the 396 recorded in 2019. For comparison, reports from a global insurance and reinsurance company showed 2020 losses from natural hazards increased by 25% from 2019 [9].

According to CRED the most common type of disaster in 2020 was flooding with 201 events, 23% higher than the average for the previous decade, followed by storms, landscapes and earthquakes. The geographical spread shows that 41% of disaster events occurred in Asia, with 23% in the Americas, 21% in Africa, 10% in Europe and 5% in Oceania. During 2020, flooding resulted in the deaths of 6,171 people, impacted 32.2 million people and created economic losses of US$ 51.3 billion; storms resulted in the deaths of 1,742 people impacted 45.5 million people and created economic losses of US$ 92.7 billion; landslides resulted in the deaths of 512 people, impacted 200,000 people and caused economic losses of US$100 million.

2021 has also seen significant disaster events with wildfires in various parts of the world, the heat dome over North America, flooding in China and Europe, Hurricane Ida impacting Louisiana in the USA, and the earthquake in Haiti to name a few. For information, by the start of 08 October 2020, there had already been 18 weather/climate disaster events reported by the US National Centers for Environmental Information (NCEI) with losses exceeding $1 billion each, to impact the United States in 2021 [10]. These events included 1 drought event, 2 flooding events, 9 severe storms, 4 tropical cyclones, 1 wildfire, and 1 winter storm. These have resulted in 538 deaths and had significant economic impact on the communities impacted. For comparison, the 1980–2020 annual average is 7.1 events and for the last five years the average is 16.2 events.

The last two years have been challenging for the world, and while these simple numbers give an outline of what has happened in terms of disasters, the pandemic will have also influenced the information in terms of potential underreporting, difficulties in determining the causality of losses, and the multiplying effect of having a disaster during a pandemic, which is likely to exacerbate all types of impact and losses.

While the number of disaster events continues to rise, the WMO report does have an element of hope: That the death toll from the weather, climate and water extremes have fallen significantly over the last 50 years due to the introduction of early warning systems. From 50,000 deaths a year in the 1970s, to 20,000 a year in the 2010s, the world has become better at reducing the number of lives lost to disaster events.

There is still more that can be done and it is important that all types of industries come together and do whatever they can to support the people and communities affected by disasters. This Pilot aims to add value and demonstrate benefits to the continuing effort to save more lives and reduce the impact of disaster events.

4.  Use of Geospatial Information in Disaster Response

Geospatial data can be defined as data that describes objects, events or features using a location on the Earth’s surface. The simplest form of presenting such information is on a map. While the earliest known maps began with the Greeks and Babylonians in the 6th Century BC, the use of geospatial data in disasters is more recent. Arguably, one of the first known uses was in 1854 when Dr. John Snow mapped, by hand, the deaths from a cholera outbreak in London. His map allowed him to see a pattern others had not noticed. When combined with local knowledge and other statistical analysis this map enabled him to determine the source of the outbreak. This work, emphasizing the need for a multidisciplinary approach, was credited with contributing significantly to the containment of the outbreak, saving many lives, and changed the way geospatial data could be used in disasters by joining the pattern of a disease to a location.

Earth Observation (EO) began around the same time as Dr Snow’s map when Gaspard-Felix Tournachon took photographs of Paris from his balloon in 1858. However, it was a century later that satellites were used to make observations of the Earth. In 1959 the Explorer VII satellite launched and the heat reflected by the Earth could be measured, and in 1960 the TIROS 1 weather satellite began producing daily cloud formations. The game changer that started the EO industry was the launch of NASA’s Earth Resources Technology Satellite in 1972, the first real mapping satellite. This satellite was later renamed to Landsat-1 beginning what has developed, to date, into an almost fifty-year archive of satellite observations of the Earth. Other space agencies around the world have also launched EO missions including the European Space Agency (ESA) who are involved with the European Union’s Copernicus program, and the Japanese Space Agency (JAXA) that have the National Security Disaster ALOS-3 and ALOS-4 missions, and the Canadian Space Agency (CSA) whose RADARSAT series of satellites support disaster monitoring activities.

As summarized by Pixalytics, as of the end of 2021, in their review of the Union of Concerned Scientists (UCS) satellite database. In total, just over a quarter of the world’s countries have control over at least one EO satellite. The USA & China manage two-thirds of the EO satellites; see Figure 1. There are just under 20 countries that only have access to one satellite. Over the last four years, the number of EO satellites orbiting the planet has grown 70%, and the number of countries controlling such satellites has grown by 39%. In addition, a number of multinational missions offer data access to other countries; for example, the Landsat and Copernicus programs offer global data freely available to anyone, making EO data more accessible. The increasing role of private companies with commercial EO satellites has increased significantly since the first one launched in 2000, and many of these also offer data services which can again broaden access at a cost. Geospatial data can also be provided from aircraft and increasingly from remotely piloted drones.

Number of EO satellites

Figure 1 — Number of EO satellites as of the end of 2021, extracted from the Union of Concerned Scientists satellite database

Around the same time as satellites were first launched, the second big development in geospatial data was the advent of computers, and the introduction of Geographic Information Systems (GIS). GIS made it much easier to map, combine, analyze and visualize geospatial information, although initially the hardware and software were expensive. Despite these developments, it was not until the early 1980s that the potential for using GIS in disaster situations was recognized. After Hurricane Andrew caused devastation to Florida in 1992 the Federal Emergency Management Agency (FEMA) committed to setting up a GIS for mapping damage and analyzing community demographics (Dash (1997)). This program soon developed into supporting areas such as Public Assistance and Hazard Mitigation.

The use of satellite remote sensing in disaster management situations took a step forward with the signing of the International Charter: Space and Major Disasters on the 22nd October 2000 by ESA, the French Space Agency (CNES) and the CSA. Currently, there are 17 contributing members including the US Geological Survey (USGS) and the National Oceanic & Atmospheric Administration (NOAA). This charter is triggered when a disaster situation occurs and makes satellite data available from different space agencies around the world to the teams responding and managing the specific disaster. Since its inception the Charter has been activated for 692 disasters in 127 countries, and during 2020 it was activated 55 times in 33 countries.

In addition, the European Union Copernicus Program has an operational service called the Emergency Management Service (EMS) which provides on-demand information for selected emergency situations including floods and droughts across the globe; as well as for wildfires in European, North Africa and the Middle East.

All of these developments mean that geospatial data can be made available to help with preparing for, responding to, and mitigating impacts of disasters, as well as supporting post event recovery. Some examples of the type of data that might be made available are:

Satellite, other geospatial and sensor data combined in a GIS offers the potential to provide disaster responders with invaluable information to support decision making and directly help field responders on the ground with the implementation of the response. This enhances the possibility of saving more lives and helping more people in disaster scenarios. Unfortunately, while the idea is great in principle, there are a number of practical issues which currently prevent the best use of these resources.

4.1.  Gaps With Using Geospatial Data

There are a number of challenges with using geospatial data within a disaster response situation, and most relate to the need to have rapid access to the right information:

  • Formats

    • Geospatial datasets often come in a variety of formats, and to integrate this variety often requires some, or all, of the datasets being converted to a format that can be managed within the GIS.

  • Time and Spatial Resolution limitations

    • Optical satellite sensors, which operate like cameras or your eyes, cannot see through clouds. This makes it more difficult to get accurate information about what is happening on the ground during floods or storms. Although microwave data can see through the clouds, this is more complicated data to use.

    • Satellite images are often only acquired over an area every few days as the satellite orbits the Earth. Some constellations can capture an image every day. However, this is still only a snapshot each day and not real-time information. For example, a satellite might completely miss a flash flooding event as the water can rise and drop between when the satellite images acquisitions.

    • Satellite images are observations of what has happened. Depending on when the imagery was acquired the actual situation on the ground may be different to what the image shows. This is called data latency.

    • Spatial resolution of satellite images is limited. While some satellites can see items on the ground which are less than a meter in size, others can only see things that are bigger than 15 or 30 meters. This may mean the images may not be able to detect the level of detail on the ground that the disaster responders require.

  • Infrastructure Availability

    • Some satellite images such as those from Landsat or Copernicus are free for anyone to use, while others, such as those from commercial sources, are not free or have restrictions placed on their use. Unless there is an agreement in place prior to an event, securing funding and purchasing such images in a disaster is difficult. So some data may not be available. Although there are various Disaster Charters within the industry whereby certain operators may make their data freely available when a disaster occurs, it can still be challenging to get permission to have access to the data.

    • Similarly, the cost of GIS software and licenses may also limit the number of people who can access the data. Available free GIS software can help mitigate this limitation.

  • Synthesizing large volumes of data

    • Satellite images and maps are large data files and in disaster situations field responders are likely to be working with poor internet and phone signals. They simply may not be able to download the information they need. Processing the data into smaller files is helpful to overcome this issue.

  • Extracting useful information from satellite images is not straightforward and requires a level of subject matter knowledge and expertise.

  • Equally interpreting data from satellites or models can take knowledge and experience. For example, models predict what might happen based on the information they have. This may be very different to what actually happens. It’s important that this element of uncertainty in what models are predicting is understood and communicated.

Put together, all of these issues mean that often 80% of time spent is on accessing and preparing geospatial data for disaster management and response, rather than using it! This includes getting permission to use it, getting it into the right format, correcting errors, getting the computing resources to process it, etc.

Therefore to address these issues, the aim of the Pilot is to provide Analysis Ready Data (ARD) for people who have the skills to use it, together with Decision Ready Indicators (DRI) from which decisions can be made and actions taken. The aim is to provide the right information to the right people at the right time in an easy-to-understand format to enable informed decisions on disaster management and response activities to be made.

5.  Future Vision of Using Geospatial Data to Support Disaster Response

The full ambition of the OGC Disaster Pilot (Pilot) is to have data ready and available within the ‘golden hour’ of the disaster – the first sixty minutes. This is the key time for saving lives and response actions taken within this period will profoundly affect the future outcomes of the overall response. However, it is challenging to provide data within the golden hour as the overall system needs to be smooth and fast!

The vision is that when a disaster occurs there will be a trigger to activate the response process. At that time the organization/individual responsible for coordinating the disaster response will be able to log onto a website and confirm the disaster they have, and this system will then highlight a series of relevant indicators, data products, and co-operating data providers. These providers will be tasked with supplying the relevant geospatial data in relation to those indicators, or the indicators themselves. The data will be transferred to either the responding organization’s Disaster Portal (a Geographical Information System (GIS) or similar) or to a selected externally provided system, where the data can be visualized and decision-driven data made available.

Users will then be able to log into a Disaster Portal via either a computer or mobile device, and search for the information that they want or need, from the indicators and datasets available within the portal. The best available data of the type requested will be provided to the user in the most useful or selected, format. This might be:

These outputs will enable users to rapidly integrate the data with the local knowledge they have, and/or act on the information directly to make decisions on how to respond to the unfolding disaster scenario.

The data available will be continually updated and improved as new datasets become available or as first responders provide ground-level observations, aiming always to provide disaster responders with the most up to date and accurate information available. When data is provided as a standards-based service, it is immediately available and updated within any GIS mapping environment that supports the standard.

This is the future vision the Pilot was focused on. However, it is acknowledged that to be able to use this solution both the potential users and data providers need to be ready to participate.

6.  What Is Readiness?

Readiness is the state of being fully prepared. In this case of disaster response, it is the state of being fully prepared to take part in the vision of a disaster response ecosystem as defined and demonstrated through the OGC Disaster Pilot 21 (Pilot). As described in Clause 5, the objective is to improve the number and speed of data-driven decisions during the disaster response. This means that once activated, the geospatial data providers within this ecosystem will make a large amount of geospatial data available. However, to make it useful and decision-ready these datasets need to be able to be accessed, processed, analyzed, visualized, and communicated to field responders in a very short amount of time.

This process is not something that can begin when a disaster occurs from either a data provider or a data user point of view. There will be too many things to resolve such as data formats, license agreements, geospatial systems, analysis skills, symbols and colors to be used in the visualization and so on. By the time these are resolved, the disaster situation will be well underway and responses will be happening without the geospatial data input.

To be part of the future Pilot framework, both data providers and data users need to be prepared to take part, and this means making a series of agreements. However, this cannot be a set of agreements between individual data providers and users, nor can it be one single solution that everyone has to fit within. Instead, it requires a set of agreed operating approaches and standards such that, for example, the data providers know the format they need to provide data, thereby enabling users to immediately integrate that data within the system they are operating. These standards will enable the smooth and rapid delivery of information to enhance decision support.

Although not developed within this Pilot, one suggestion is to set out a series of Operational Readiness Levels (ORLs), similar to those developed by Earth Science Information Partners (ESIP) for making Earth science data more trusted (, particularly by non-technical decision makers. This would identify the steps and operating standards that both data providers and users will need to take to be able to fully participate.

The next section of this report focuses on user readiness and will be ‘technology-lite’ with minimal jargon and technical language. The intent is to demonstrate how this might work and what it can deliver. However, it is acknowledged that some parties might be both data users and data providers. Therefore, the companion Provider Guide will offer a more technical description of the proposed solution and requirements.

7.  What Is User Readiness?

This guide is for users — practitioners, decision-makers, and responders who want to both make use of Analysis Ready Datasets (ARD) and Decision Ready Indicators (DRI), and influence how they are developed for maximum usefulness in action. This section describes a series of steps that users should undertake to be in a position to fully participate in the Disaster Response ecosystem envisaged by the OGC Disaster Pilot 2021 (Pilot):

7.1.  Step 1: Preparation of Foundation Layers

Users need to develop, and maintain, the foundation layers of local geospatial information into which the data from the providers can be integrated. This would include elements such as street maps, building footprints, elevation models, satellite imagery, key buildings such as hospitals, electricity substations, land cover, water bodies, etc.

These foundation layers are critical as they form the framework for the ARD and DRI to be displayed, and without these layers, it will be a struggle to analyze, interpret and transform the additional data into decisions and actions. For example, if the indicators show an area of a city is going to be flooded, the response will be very different if that area is a park, a residential area or a hospital.

Care and attention must also be given to the currency, accuracy and intended use of the data acquired. For example, applications such as landslide or flood modelling and impact analysis require high resolution elevation models which may be difficult to acquire depending on the spatial data infrastructure available for a given region.

The Pilot has not specified a particular standard for the Foundation Layers, as there are a number of worldwide standards already available. While they are all different, there is considerable crossover within these standards and many of the layers are very similar. Current example foundation layer standards include:

In addition, although not a standard, the OGC’s Health Domain Working Group has produced a Health Spatial Data Infrastructure Concept Development Study Engineering Report looking specifically at what Foundation would be useful in a health emergency. This report identified 8 spatial datasets, 12 local government datasets & 7 national datasets which would be useful to have as foundation and background layers.

Using any of these standards as the basis for identifying foundation layers would be a beneficial step, high resolution data (HRD) to improve accuracy. The selection of a preferred standard may be helpful in the future.

One aspect of data preparation that should not be underestimated is the amount of work involved to acquire the source data for a given disaster response effort. A typical response involves a wide range of datasets that must be researched, accessed, filtered to the area of interest, and transformed into a form suitable for the user’s application environment. This process is sometimes called ETL (extract transform and load), and can involve significant effort in terms of data wrangling. Ideally this effort is supported by spatial data infrastructure based on open standards, data services via open Application Programming Interfaces (APIs) and domain specific conventions. More commonly, the reality is more of a mix, so often significant time and effort is required to procure the required datasets and render them into a form that is usable within the user’s disaster response application. The importance of these standards in the context of disaster response is discussed in the next section.

Typically there are a range of tools available to support this data extraction, integration and conversion process. These include both specialized data integration platforms or middleware, and custom applications developed with open source tools and libraries such as GDAL and OGR. For this pilot, the FME spatial data integration platform from Safe Software was used to perform many data extraction and transformation tasks. This included workflows where proprietary source files were read and written to open standards formats such as OGC GeoPackage and GeoJSON [4] for use by other components (see Figure 2). FME was also used to load datasets onto the pilot geoportal: GeoNode, which in turn hosted these datasets for download or delivery via OGC-conformant interfaces such as those implementing the Web Feature Service (WFS) standard (using GeoServer).

Besides basic format conversion, key aspects of any data integration platform is the ability to perform geometry and schema transformation. The native OSM schemas are complex and nested, so FME was used to flatten this into a more relational or GIS friendly structure. Time series raster datasets were converted to vector features and loaded into GeoPackage tables to make them easier to integrate with other GIS workflows within downstream tools. Finally coordinate transformation is usually required to bring the application datasets into a common reference frame.

FME data integration platform - OSM and GeoJSON to GeoPackage loader

Figure 2 — FME data integration platform - OSM and GeoJSON to GeoPackage foundation data loader

7.2.  Step 2: Understand and Implement Standards

To be able to rapidly integrate and transform data into useful decision-ready information, it’s necessary to eliminate all the unnecessary challenges of data management. This will include aspects of data formatting, visualization methods, symbols to use on maps, etc. This is critical to cut the time it takes for data to be ready to be used for decisions and actions. The use of standards will enable these processes to happen effectively and efficiently.

Without such standards, the potential for wasted time on data wrangling and preparation is high, and even worse the potential for inefficient, incorrect, or even wrong disaster response decisions increases. The standards will also help non-technical decision makers, who require trusted data to be used to drive decision making, but cannot find what they need, due to the varied and complex semantics of hazards and disasters.

Within the Pilot, a number of key standards were identified as being important for user readiness. These are described below with technical examples of such standards.

  • Ensure that data and imagery use formats that can be easily used, integrated and visualized by any GIS, such as Cloud Optimized GeoTIFF (COG).

  • Ensure that when publishing geospatial data, it is done in a structured way to best support web-based searching. For example, using JavaScript Object Notation — Linked Data (JSON-LD).

  • Generation of catalogs and self-describing data sets which will help users find and understand the accessing and processing of data, for example, GeoPackage and SpatioTemporal Asset Catalog (STAC) [5].

  • Platforms which visualize and communicate information should do so using standard formats such as Web Coverage Service (WCS)[6], Web Feature Service (WFS)[2] and Web Map Tile Service (WMTS)[3]. These standards also make the data much more shareable across platforms

7.3.  Step 3: Utilize and Implement Indicators and Supporting Indicator Recipes

Once the required foundation data layers are assembled, the next step is to determine what decision ready indicators are required to support disaster managers and responders in the field. As described above, DRI are built on a foundation of ARD, and enriched by the context of foundation data described in Step 1 above. There can also be Integration Ready Data (IRD), which is an intermediate step between ARD and DRI, that could include flood extents and disease density.

While data providers may publish a set of primary ARD, IRD or DRI, often there is the need for the disaster response users to take this primary information and develop secondary indicators more closely calibrated to day to day needs of their disaster response mission.

For example, the data provider may publish disaster extents (flood or fire extent) plus areas for evacuation or submerged roads. The disaster response user with some GIS skills may need to take this information and develop more precise information as to what areas to evacuate first (hospitals and high density residential), what areas to protect (critical infrastructure) or what routes may be passable for specific types of emergency vehicles. Also disaster managers often benefit from composite indicators and dashboards that combine a range of indicators and IRD to build a more comprehensive operational picture of the overall disaster and corresponding response efforts.

The Provider Guide describes in more detail the data value chain that was developed in the context of this pilot to take source datasets, process them into ARD, IRD and ultimately DRI to drive the outputs described below. Recipes describe the specific procedure for combining source foundation layers, dynamic observations related to the disaster context and analyzing this to produce ARD, IRD and DRI. However, even from a user perspective, the datasets published by data providers can often be seen just as a starting point.

For this pilot, several key indicators and associated recipes were developed related to flood severity, landslide risk and pandemic impacts. For example, for the flood scenario, flood model grid outputs from RSS Hydro were processed into vector flood contours using FME and stored in GeoPackage IRD. Skymantics was then able to take this IRD and compute indicators about transportation routes taking into account flood depth not just extents. In addition, users could utilize this same flood depth data to generate other related indicators, such as areas to prioritize for evacuation.

Key to this is the understanding that often one indicator may be used to support other indicators downstream. Also, the development of effective indicators and supporting IRD often require frequent feedback from users, and responsiveness from data providers, to ensure that the data and information being provided is suitable and tuned to the needs of the end users. Finally, any recipe implementation should use approaches that promote reuse and automation to the extent possible. In this way rapidly evolving disasters can be met with timely indicator updates and associated response actions. A model based, reuse and automation orientation also makes it more practical for tools and recipes to be applied to new contexts. In this way, the disaster response community as a whole can benefit from the development of these indicator-based tools and systems. Note that ARD, IRD and DRI in the context of this pilot are described in more detail in the Clause 8, and in the adjoining annexes.

7.4.  Step 4: Determine the Method For Delivering Outputs

Receiving a large amount of data, and then analyzing, processing and visualizing the data is only the first half of the work, the second is getting the outputs of that work to the people managing the disaster response and the field responders on the ground via their mobile phones or similar devices.

There are a variety of solutions for this and the Pilot is not recommending one, nor is it suggesting that the solution would be based around a single technology. Instead by establishing a set of required standards for data sharing, it will enable data to be interoperable and reusable across any platform. Solutions could be provided open source, commercial, or even using existing internal infrastructure.

The key element is that the user organization has a solution where they can upload the decision-ready indicators for users to access. There is no single answer to this question and the preferred solution will depend on the organization’s infrastructure, financial pressures, technical skills, etc.

Within the Pilot several external platforms were tested including:

  • GeoCollaborate – This platform, developed by StormCenter Communications offers an option for a cross-platform real-time collaborative environment led by a subject matter expert, engaging with a series of followers who are actually receiving the data on any platform or device. This offers the potential for many people to interact with the same shared information at the same time leading to faster situational awareness and decision making accordingly. The solution can connect any device with an internet connection and a browser, allowing the user to see and interact with the information in real time while also enabling leaders to hand-off control so additional datasets can be shared or turned off. Since the data is not downloaded and saved on everyone’s device it can be used by people on the on-ground with limited bandwidth. GeoCollaborate is not screen sharing but delivers actual cross platform geospatial interoperability to any number of participants wherever they are located. GeoCollaborate can access data from any portal, hub, geoplatform, server or share uploaded data across all follower’s web maps.

Figure 3 shows a screenshot from a GeoCollaborate instance with the leader’s screen on the left, and the follower’s on the right. The image itself is sharing data for Rimac River in Peru, and includes a flood extent and clinic location datasets produced by via their geoplatform in real-time.