Published

OGC Engineering Report

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

Published

Document number:21-074r2
Document type:OGC Engineering Report
Document subtype:
Document stage:Published
Document language:English

License Agreement

Use of this document is subject to the license agreement at https://www.ogc.org/license




I.  Abstract

Disasters are geographic events and, therefore, geospatial information, tools, and applications have the potential to support the management of, and response to, disaster scenarios to save lives and limit damage.

The use of geospatial data varies significantly across disaster and emergency communities, making the exploitation of geospatial information across a community more difficult. The issue is particularly noticeable when sharing between different organizations involved in disaster response.

This difficulty can be mitigated by establishing the right processes to enable data to be shared smoothly and efficiently within a disaster and emergency community. To do this requires the right partnerships, policies, standards, architecture, and technologies to be in place before the disaster strikes. Having such a set-up will enable the technological and human capabilities to quickly find, access, share, integrate, and visualize a range of actionable geospatial information, and provide this rapidly to disaster response managers and first responders.

For over 20 years, the Open Geospatial Consortium (OGC) has been working on the challenges of information sharing for emergency and disaster planning, management, and response. In Disaster Pilot 23 (DP23) the aims were to:

The Disaster Pilot Provider Guide describes the technical requirements, data structures, and operational standards required to implement the data flows or tools developed in DP23 and Disaster Pilot 21 (DP21) where participants have worked on disaster scenarios relating to the following.

Case Studies have focused on the hazards of drought in Manitoba, Canada; wildland fires in the western United States; flooding in the Red River basin, Canada; landslides and flooding in Peru; and pandemic response in Louisiana, United States. The participants have developed a series of data specific workflows to generate either Analysis Ready Datasets (ARD) or Decision Ready Indicators (DRI) alongside a number of tools and applications to support data discovery, collection, or visualization.

Annex A describes the tools and applications developed within the Pilots along with technical details and the benefits offered similar to the data flows. The Guide finishes with details of future possibilities and where the Disaster Pilot initiatives could focus next. Annexes B to E give descriptions of the data flows developed, including technical details of input data, processing and transformations undertaken, standards applied, and outputs produced with details of the aspect of disaster management or response supported, benefits offered, and the type of decisions assisted with.

The Provider Guide is one of three Guides produced within DP23 together with the User Guide and the Operational Capacity Guide. While the Guides are separate individual documents, the Provider and User Guides work together, mirroring each other in terms of structure. The Operational Capacity Guide is a stand-alone document effectively underpinning the other two.

II.  Keywords

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

Disasters, Natural Hazards, Analysis Ready Data, ARD, Decision Ready Indicators, DRI, Drought, Wildland Fire, Flooding, Landslides, Pandemic, Emergency Response, geospatial, ogcdoc, OGC document, DP23, DP21, Disaster Pilot, Provider Readiness Guide

III.  Submitting Organizations

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

IV.  Submitters

All questions regarding this document should be directed to the editors or the contributors.

NameOrganizationRole
Andrew LavenderPixalytics LtdEditor
Sam LavenderPixalytics LtdEditor
Ryan AholaNatural Resources CanadaContributor
Stefano BagliGECOsistemaContributor
Omar BarrileroEuropean Union Satellite CentreContributor
Dave BorgesNASAContributor
Harrison ChoUSGS-GeoPathwaysContributor
Paul ChurchyardHSR.healthContributor
Rhett CollierDualityContributor
Antonio CorreasSkymanticsContributor
Katharina Demmich52 NorthContributor
Patrick DionEcereContributor
Rich FrazierUSGS-GeoPathwaysContributor
Theo GoetemannBasil LabsContributor
Ajay K GuptaHSR.healthContributor
Omar HeribaUSGS-GeoPathwaysContributor
Dean HintzSafe SoftwareContributor
Josh HusseyCompusult LimitedContributor
Jérôme Jacovella-St-LouisEcereContributor
Amy JeuGeospatial Information Systems and Mapping OrganizationContributor
Dave JonesStormCenter CommunicationsContributor
Brennan JordanUSGS-GeoPathwaysContributor
Travis KehlerDualityContributor
Albert KettnerRSS-HydroContributor
Alan LeidnerGeospatial Information Systems and Mapping OrganizationContributor
Adrian LunaEuropean Union Satellite CentreContributor
Jason MacDonaldCompusult LimitedContributor
Niall McCarthyCrust TechContributor
Vaishnavi RaghavajosyulaUSGS-GeoPathwaysContributor
Carl ReedCarl Reed and AssociatesContributor
Sara SadriUN UniversityContributor
Johannes Schnell52 NorthContributor
Guy SchumannRSS-HydroContributor
Sumit SenIIT BombayContributor
Sunil ShahDualityContributor
Harsha SomayaUSGS-GeoPathwaysContributor
John Christian SwansonUSGS-GeoPathwaysContributor
Ian TobiaUSGS-GeoPathwaysContributor
Marie-Françoise VoidrotOpen Geospatial ConsortiumContributor
Jiin WenburnsGISMOContributor
Colin WithersCompusult LimitedContributor
Peng YueWuhan UniversityContributor

1.  Introduction

Disasters are geographic events in specific locations that impact the people, economy, and society in those and surrounding areas — often tens, or even hundreds, of miles away. For this reason, geospatial information has been shown to be effective in supporting both the understanding of, and the response to, disaster scenarios.

Geospatial tools and applications have the potential to save lives and limit damage, and the world is becoming better at using these resources. Unfortunately, the ability to manage, access, share, use, reuse, and exploit geospatial information and applications is often limited. This can be, in particular, between organizations as the right processes have not been established for these processes to happen smoothly and efficiently within disaster and emergency communities. Establishing such processes requires partnerships, policies, standards, architecture, and technologies to be in place before the disaster strikes.

For over 20 years the Open Geospatial Consortium (OGC) has been working on the challenges of information sharing for emergency and disaster planning, management, and response. The Disaster Pilot activities are part of the OGC Collaborative Solutions and Innovation Program (COSI) with the aim to address the gap, and provide support and guidance on how disasters and emergency communities can enhance sharing and use of geospatial information and applications.

Disaster Pilot 23 (DP23) is the latest in a series of initiatives focussed on:

This Provider Guide aims to provide data collectors, processors, and publishers with detailed technical requirements, data structures, and operational standards required to develop and offer data workflows and tools within the ecosystem of the OGC Disaster Pilot initiatives. The Guide also supports in the preparation and coordination needed to leverage standards-based cloud computing and real-time data sharing and collaboration platforms in support of disaster management and response efforts.

In addition, the Provider Guide gives emergency management, together with any other supporting stakeholders, information technology support functions and technical details to understand how to implement any of the workflows or tools developed in disaster and emergency user communities.

Geospatial information offers huge potential resources to enable disaster and emergency communities to enhance planning, prediction, and response to disaster events, helping save more lives and reducing the impact of disasters on communities.

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

raw data that have had some initial processing, created in a format that can be immediately integrated with other information and used within a Geographic 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

realistic and measurable criteria

2.1.5. Lidar

light detection and ranging ALTERNATIVE

common method for acquiring point clouds through aerial, terrestrial, and mobile acquisition methods

2.1.6. GeoNode

web-based platform for deploying a GIS

2.1.7. GeoPackage

open, standards-based, compact format for transferring geospatial information

2.1.8. GeoRSS

designed as a lightweight, community driven way to extend existing RSS feeds with simple geographic information

2.1.9. GeoServer

Java-based server that allows users to view and edit geospatial data

Note 1 to entry: Using open standards set forth by the Open Geospatial Consortium (OGC), GeoServer allows for great flexibility in map creation and data sharing.

2.1.10. Geospatial Data

data that include information related to a location, that can be used to map objects, events, and anything else with a specific geographic location

2.1.11. JSON-LD

JavaScript Object Notation — Linked Data ALTERNATIVE

lightweight linked data format based on JSON

2.1.12. Jupyter Notebooks

open-source web application that allows the creation and sharing of documents that contain live code, equations, visualizations, and narrative text

2.1.13. Radar

radio detection and ranging ALTERNATIVE

detection system that uses radio waves to determine the distance (range), angle, or velocity of objects

2.1.14. REST API

Representational State Transfer Application Programming Interface

Note 1 to entry: Commonly known as REST API web service.

Note 2 to entry: When a RESTful API is called, the server will transfer a representation of the requested resource’s state to the client system.

2.1.15. SAR; Synthetic Aperture Radar

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

ACD

Amplitude Change Detection

AI

Artificial Intelligence

AMSR-E

Advanced Microwave Scanning Radiometer for EOS

API

Application Programming Interface

AR

Augmented Reality

ARD

Analysis Ready Data

ASMR-2

Advanced Microwave Scanning Radiometer 2

AWS

Amazon Web Services

BCSD

Bias corrected and spatially downscaled

C3S

Copernicus Climate Change Service

CAMS

Copernicus Atmosphere Monitoring Service

CDI

Combined Drought Indicator

CDS

Climate Data Store

CEMS

Copernicus Emergency Management Service

CNES

French Space Agency

COG

Cloud Optimized GeoTIFF

CONIDA

National Commission for Aerospace Research and Development’s, Peru

COSI

OGC Collaborative Solutions & Innovation Program

CQL2

Common Query Language

CRS

Coordinate Reference System

CSA

Canadian Space Agency

CSW

OGC Catalog Service for the Web

DARSIM

Disaster Augmented Reality Simulation Table

DEM

Digital Elevation Model

DP21

Disaster Pilot 21

DP23

Disaster Pilot 23

DRI

Decision Ready Indicator

DT

Digital Twin

DTES

Digital Twin Encapsulation Standard

ECMWF

European Centre for Medium-Range Weather Forecasts

EDR

Environmental Data Retrieval

ENSO

El Niño/Southern Oscillation

EO

Earth Observation

ESA

European Space Agency

ESIP

Earth Science Information Partners

ETL

Extract, Transform and Load

FAIR

Findability, Accessibility, Interoperability, and Reuse of digital asset

FAPAR

Fraction of absorbed light by the plants

FME

Feature Manipulation Engine (Safe Software)

GDO

Copernicus Global Drought Observatory

GEPS

Global Ensemble Predication Service

GIS

Geographic Information System

GISMO

New York City Geospatial Information System & Mapping Organization

GPM

Global Precipitation Measurement Mission

ICU

Intensive Care Unit

IR

InfraRed

JAXA

Japan Aerospace Exploration Agency

JSON

JavaScript Object Notation

JSON_LD

JavaScript Object Notation — Linked Data

LEO

Low Earth Orbit

LSTM

Long Short-Term Memory

MAE

Mean Absolute Error

MAPE

Mean Absolute Percentage Error

ML

Machine Learning

MRLC

Multi-Resolution Land Characteristics

MSC

Meteorological Service of Canada

MTC

Multi-Temporal and Coherence

NAOMI

New AstroSat Optical Modular Instrument

NDVI

Normalized Difference Vegetation Index

NetCDF

Network Common Data Form

NIR

Near-InfraRed

NLCD

US National Land Cover Database

NNet

Neural Network

NOAA

US National Oceanic and Atmospheric Administration

NRCan

Natural Resources Canada’s

NRT

Near-Real-Time

OGC

Open Geospatial Consortium

OLI

Operational Land Imager

ORLs

Operational Readiness Levels

OSM

OpenStreetMap

PDSI

Palmer Drought Severity Index

PHC

Public Health Center

PPE

Personal Protective equipment

RCM

RADARSAT Constellation Mission

RMSE

Root Mean Squared Error

S3

Amazon Simple Storage Service

SAR

Synthetic Aperture Radar

SatCen

European Union Satellite Centre

SDI

Spatial Data Infrastructure

SMA

Soil Moisture Anomaly

SPDI

Standardized Palmer Drought Index

SPEI

Standardized Precipitation Evapotranspiration index

SPI

Standardized Precipitation Index

SPoG

Single Pane of Glass

SRTM

Shuttle Radar Topography Mission

SST

Sea Surface Temperature

STAC

SpatioTemporal Asset Catalog

TIRS

Thermal Infrared Sensor

TRMM

Tropical Rainfall Measuring Mission

UAV

Uncrewed Aerial Vehicles

USGS

US Geological Survey

VGI

Volunteered Geographic Information

VIIRS

Visible Infrared Imaging Radiometer Suite

VR

Virtual Reality

WCS

Web Coverage Service

WFS

Web Feature Service

WHO

World Health Organization

WKT

Well-Known Text

WMS

Web Map Service

WMTS

Web Map Tile Service

WPS

Web Processing Service

WUI

Wildland-Urban Interface

XR

Extended Reality

3.  Relationship Between Guides

The Provider Guide is one of a trilogy of Guides being developed through Disaster Pilot 2023 (DP23) alongside the User Guide and the Operational Capacity Guide. These three guides are shown in Figure 1.

Fig-guides-relationship

Figure 1 — Three Guides

The details of the three Guides are as follows.

Provider Guide

User Guide

Operational Capacity Guide

The Guides work together, with each individual Guide focusing on the key information for each Guide’s specific audience and providing signposting to further details should it be required. Figure 2 gives an overall structure for the Guides.

Fig-detail-guide-relationship

Figure 2 — Detailed Guides relationship.

4.  Use of Geospatial Information in Disaster Response

Disaster management is generally understood to consist of four phases: mitigation, preparedness, response, and recovery. Mitigation describes activities aimed at reducing the occurrence of emergency situations, preparedness focuses on active preparation, response is the acute phase occurring during and after the event, and recovery covers a wide range of processes that support getting back to a state of acceptable operation. While all phases are interrelated and important, the response and recovery phrases are often viewed as the most critical in terms of saving lives. The timely provision of geospatial information can greatly help in the decision-making process, save lives, and aid those affected.

4.1.  Geospatial Data

Geospatial data can be defined as data that describe objects, events, or features using locations on the Earth’s surface. The simplest form of presenting such information is a map. Whilst the earliest maps began with the Babylonians and Greeks in the 6th Century BC, the use of geospatial data in disasters is a bit more recent. Arguably, one of the first uses was in 1854 when Dr. John Snow mapped, by hand, the deaths from a cholera outbreak in London.

The term geospatial data covers a lot of different sources of data which have spatial references, meaning the data have geographical references, which give the data locations. There are many types of geospatial data, and all could offer benefits in a disaster scenario depending on the circumstances. Examples of the types of geospatial data that could be available include the following.

  • Satellite data — can include optical, thermal, radar or lidar data

  • Airborne imagery/photography — could be collected by aircraft, helicopters, or drones and can also include optical, thermal, radar, or lidar data

  • Oblique angle photography

  • Building or Computer Aided Design drawings

  • 3D Renderings

  • Citizen Science data – namely, data collected by first responders or general citizens, usually on handheld devices, giving precise snapshots of what is happening at specific locations.

The type of disaster, the location, or the information required, will determine which data sources may be helpful. However, it is unlikely that one single data source will answer all the questions about the disaster. The way problems are often solved is through the integration of multiple data sources, and it is these combinations of datasets and looking at the data in different ways that give additional insights.

Together, geospatial data and technologies such as Geographic Information System (GIS) offer the potential to provide first responders with invaluable information. This information can be used to support both the planning and the implementation of the response, through maps and information, directly to the field responders on the ground, improving awareness of the current situation. Therefore, providing access to these technologies allows saving lives and helping people respond to disaster events. The following look at some of the more common types of geospatial data in more detail.

4.1.1.  Satellite Data

Earth Observation (EO) started 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 with the launch of the Explorer VII satellite in 1959 that satellites were used to make observations of the Earth. The first real mapping satellite was NASA’s Earth Resources Technology Satellite launched in 1972, later renamed to Landsat-1. To date, Landsat offers a fifty-year archive of satellite observations of the planet. Other space agencies around the world have also launched EO missions including the European Space Agency (ESA) which is involved with the European Union’s Copernicus program, the Japanese Space Agency (JAXA) that has the National Security Disaster ALOS-3 optical and ALOS-4 microwave missions, and the Canadian Space Agency (CSA) whose RADARSAT series of satellites support disaster monitoring activities.

While there are now lots of satellite datasets and products, both commercial and governmental, there are also some programs focused solely on supporting disaster and emergency scenarios, such as the following.

  • International Charter: Space and Major Disasters was signed on 22 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 providers around the world, giving the teams responding and managing the specific disaster access to a wide range of data. Since its inception, the Charter has been activated for over 800 disasters in 127 countries; during 2022 it was activated 51 times.

  • Copernicus Emergency Management Service (CEMS) is a European focused service that provides Disaster and Emergency communities with geospatial information to inform decision making. CEMS constantly monitors Europe to forecast, analyze, and provide information for resilience strategies. The datasets are created using satellite, in situ (ground), and model data, and offers on-demand maps, time-series, or other relevant information to better manage disaster risk.

4.1.2.  Airborne Imagery or Photography

Aircraft are often requisitioned as a core lifeline after disasters occur, as the aircraft are used to drop supplies and/or rescue survivors. As a result, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage, and low cost, i.e., photographs from the people on the aircraft. A more operational use, for example, could be for wildfire detection and monitoring, with thermal sensors being particularly useful. The initial characterization of the fire’s properties (e.g., location, size, proximity to water or inhabited areas) is critical to mounting an initial response.

Uncrewed Aerial Vehicles (UAVs), often termed drones, can also provide local situational understanding in terms of what is currently happening and/or the immediate aftermath.

Looking towards the near-term future, High Altitude Pseudo Satellites (termed HAPS) which are high altitude balloons, or constellations of micro-satellites, will increasingly offer sensor-dependent persistent coverage.

4.1.3.  Citizen Science Data

Sensors can also be found in smartphones and other devices, plus social media offers potential to collect data. As a result, any person, device, or sensor is a potential data generator and can create complex datasets, termed Big Data. The location of the sensor is expressed in a standard and readily understood form, such as latitude-longitude, street address, or position in some coordinate system. But these data may also include indirect information about location or unstructured data, where methods and tools for extracting the required information are needed. Also, although mobile applications can be a key element in improving situational awareness, a post-event observation may not provide important information on the pre-event structures and so different sources for pre- and post- event awareness must be combined. Also, any new technological ‘solution’ would tend not be used during a disaster unless the stakeholder community had already adopted and trained with the solution pre-event.

4.1.4.  Real time sensor data

While citizen science data tends to be intentionally submitted, mobile devices and other systems can collect data more automatically. Producing a continuous stream of data which can be leveraged to observe patterns and detect anomalies, and in turn detected patterns can help drive hazard indicators. These data can be collected from sensors, instruments, or devices in the environment (Internet of Things IoT).

4.1.5.  Model Data

While real time sensors give a better situational awareness of what is happening in the world today, models provide a better understanding of what could be happening given a range of assumptions. These assumptions are used to feed a model calibrated to behave in ways that approximate to some aspect the natural world. Increasingly, the importance of incorporating the results of climate models to gain a better understanding of future possibilities and hazards associated with climate change is being realized.

4.1.6.  Challenges of Using Geospatial Data

While the idea of using geospatial data within disaster and emergency situations is positive, there are several practical issues that can prevent the data being used to the greatest advantage. These challenges include the following.

For First Responders

  • Geospatial maps and data can be useful to give situational awareness, but are not comparable to having experienced boots on the ground determining what needs to be done.

  • Sharing data is challenging for first responders:

    • lack of mobile coverage and service is a big issue; and

    • data often have to be shared physically, such as via AirDrop on Apple devices.

  • Speed of update is critical in fast moving situations.

  • Offline options for data management and visualization are vital in rural areas.

For Operational Managers:

  • Data acquired are not always useful as availability can be limited by meteorological and geographical factors.

  • Emergency management agencies want value-added products such as decision support information, thematic data/images, etc., as these can be quickly integrated into the decision-making process.

  • The area impacted by an event, or within which the rescue teams operate, varies considerably from a few square kilometers for local events (landslides, earthquakes, etc.) to thousands of square kilometers for very large area events such as tsunamis, tropical storms, and floods in low-lying areas. So, data need to be at a spatial resolution that can help and support decision making.

  • Tendency to go with the tried and tested approach for data sharing and mapping applications, and a conservative approach to trying new technology.

For Data and GIS Analysts

  • Obtaining, downloading, and integrating data into local GIS systems can take too long, i.e., before the data are useful for disaster response.

  • Increases in the amount of data potentially available can be overwhelming and can inhibit the ability to efficiently manage, use, and share data.

For Satellite data operators/value adders

  • Satellite data response time is often not considered rapid enough for real-time monitoring.

  • Delivery of first crisis satellite products within 23 hours remains challenging, due to the characteristics of the satellites, their orbits, cloud cover, and “true operational” revisit time.

  • The lack of a framework to quickly supply satellite images free of charge in an emergency, which needs to include data providers (space agencies and commercial organization), data analysts (universities, research institutes, etc.), and users (disaster management organizations), can make it difficult to obtain data where credit card or financial approval is needed. These issues are partly addressed through international efforts, but activities need to continue to improve satellite data to fulfill the potential for the data to be used throughout the value chain.

For Citizen Science Data

  • Social media data have improved the efficiency and scope of disaster information communication, but at the same time, social media data also bring some misinformation. Therefore, filtering and cleaning is an important part of the disaster information and representation process. Also, social media companies are starting to monetize data and other offerings and so access may no longer be free.

  • Social engagement and participation in data sharing and reliable information are lacking in developing disaster GIS maps and 3D representation.

5.  How To Use This Guide?

The OGC Disaster Pilot 2023 (DP23) technical solution built on the success and outcomes of Disaster Pilot 2021 (described in Clause 7.2) and other OGC Collaborative Solutions and Innovation Program (COSI) initiatives.

This Guide is for existing and potential data and technical application providers, data collectors, processors, publishers, emergency management information technology support personnel, and other supporting stakeholders.

This Guide gives the detailed technical requirements of each of the tools and data workflows, together with links to persistent demonstrators showing the operating details and the benefits available to disaster and emergency communities. This information should give enough detail to enable the tools and workflows to be operated, and also give tool developers the standards needed to participate in this ecosystem.

5.1.  Tools to Support Use of Geospatial Data

Within Annex A there are a series of potential tools that can help disaster and emergency communities to gather, find, and visualize data for any type of disaster. For each tool there are:

  • a description of the tool, and what it can offer;

  • details of the benefits the tool offers, how it can support decision making, and the job roles of who would use this tool; and

  • details of how to find the online demonstrations for the tool and any collaborations undertaken as part of DP23.

5.2.  Data Workflows to Support Disaster Management & Response

A series of specific data workflows have been developed by DP23 & DP21 participants covering the following.

  • Droughts in Annex B

  • Wildfires in Annex C

  • Flooding, including landslide and pandemic impacts, in Annex D

  • Integration of Health & Earth Observation Data for Pandemic Response in Annex E

These data workflows will produce either Analysis Ready Datasets or a Decision Ready Indicator, which are described in more detail below.

Each of the data workflows within the Annexes include the following.

  • Introduction to the workflow and the risk or issue it aims to support.

  • Indicator recipe, which describes the input data used, the processing and transformations undertaken, the output data produced, and details on the technical requirements and standards used by the workflow.

  • Details of the benefits the workflow offers, the types of decisions it can support by these data, and the job roles that would use the output.

  • Details of how to find the online demonstrations for tool and any collaborations undertaken as part of DP23.

5.2.1.  Types of Users

The Pilot has identified the following four user groups.

  1. Data Analysts working for the responding organizations providing insights and information for the disaster planners or field responders. These may include data analysts, GIS analysts, and logisticians.

  2. Disaster Response Planners or Managers who lead the disaster readiness and response activities for the responding organizations.

  3. Field Responders who are on the ground responding to the disaster and reporting to the responding organizations.

  4. Affected public and communities who want direction and guidance on what to do.

Each of these user groups requires different types of data or information, at different levels, and presented in different ways.

5.2.2.  Data Set Types

The data workflows take raw data (which could be any form of geospatial data such as geographic data, satellite, or airborne data) fixed gauges or instruments, demographic and social data, health data, field observations, or citizen science data, and then undertake some processing to create one of two types of datasets; either an Analysis Ready Dataset or a Decision Ready Indicator as shown in Figure 3.

Pilot-Architecture-Overview

Figure 3 — Relationship between ARD to DRI, courtesy of Josh Lieberman (OGC).

  • Analysis Ready Datasets (ARD)

Analysis Ready Datasets are raw geospatial data to which initial processing has been undertaken to create a dataset in a format that can be immediately integrated with other information and used within a Geographic Information System (GIS) and can be interrogated by people with the right skills to gain greater insight. These data can be either visualized or further analyzed, interrogated, and/or combined with local knowledge to create information upon which decisions can be made.

+ ARD is most likely to be used by Data Analysts but could also be used by Disaster Response Planners and Managers.

  • Decision Ready Indicators (DRI)

Decision Ready Indicators are 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.

+ This information will be useful for Disaster Response Planners and Managers, Field Responders, and the Affected Public and will be able to be used without any specialist knowledge, skills, or software. DRI datasets may also be useful to Data Analysts in order to build composite or multi-stage indicators.

Note: Although these are the two main types of data envisioned throughout the Pilot it was acknowledged that datasets might exist between ARD and DRI. For example, some datasets may be considered to be actionable observations: more refined and richer than basic ARD, but without the clearly defined rules or parameters as to what action should be taken that would be necessary to consider them DRIs. The type of dataset offered by each participant should include a clear indicator recipe and/or output type in the Annexes.

6.  What is Provider Readiness?

Readiness is the state of being fully prepared and in this case, it is the state of being fully prepared to provide information to support a disaster response activity. More specifically, in terms of the OGC Disaster Pilot activities, Readiness is determining what is needed for developed tools and workflows to be operationalized by a disaster and emergency community ecosystem.

The overarching aim of DP23 was to develop flexible, scalable, timely, and resilient information workflows, together with applications and visualization tools to promote a wider understanding of how geospatial data can support emergency and disaster communities and critical disaster management decisions. To be part of the envisaged Pilot ecosystem, both data providers and users need to be prepared to take part, which means making a series of agreements. However, this cannot simply 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 need to know the format the data needs to be provided in that users can immediately integrate within the data system being operated.

Ideally, this activity would be undertaken before a disaster occurs, as starting this process once a disaster is underway will simply take time and slow down getting the geospatial data to the people that can put it to use to support the response. The elements that need to be agreed run from license agreements through to data formats, geospatial systems used, analysis skills, data aggregation and transformation methods, and even the symbols and colors to be used in the visualization, and so on.

This section of the Guide describes the four steps that providers should complete in order to be in a position to achieve readiness and fully participate in the disaster response ecosystem.

6.1.  Step 1: Understand and Implement Common Standards

As discussed, the driving force of these initiatives is to enhance the access, sharing, use, reuse, and exploitation of geospatial information and applications across, and between, organizations within disaster and emergency communities. Common standards are fundamental, as are the underpinning foundations that are needed to support data interoperability.

Using agreed technical standards alongside common data formats will ease the process of integrating data, not only within the disaster and emergency community, but also across boundaries, which will also help any new data providers wanting to offer new datasets to be clear as to what the requirements would be for any new data flows.

Without 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.

6.1.1.  FAIR Principles

OGC promotes, and encourages, the FAIR principles for data management to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets; these are as follows.

  • Findable

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

  • Accessible

Once the user finds the required data, she/he/they need to know how the data can be accessed, possibly including authentication and authorization.

  • Interoperable

The data usually needs to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

  • Reusable

The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that both can be replicated and/or combined in different settings.

6.1.2.  Geospatial Standards Within Disaster Pilot 23

The geospatial standards for each of the elements from data discovery to visualization include the following.

  • Discovery

Service(s) and associated application(s) support near-real-time registration, search, and discovery of Analysis Ready Datasets (ARD) and Decision-Ready Information and indicators (DRI), alongside contextual social-political-health datasets and local observations within impacted areas. This element includes implementations of OGC API Standards that provide structured geospatial data to support web-based searching with JavaScript Object Notation — Linked Data (JSON-LD) and include generation of metadata catalogs and self-describing datasets to aid data access and processing, e.g., SpatioTemporal Asset Catalog (STAC) and GeoPackage.

  • Data Storage and Processing in the Cloud:

Cloud-based storage, processing, and service elements support the loading, preparation, and access to individual satellite-based datasets alongside complementary geospatial datasets. Then, further application elements are deployed near to source datasets and support agile, rapid, and scalable generation of decision-ready (e.g., indicator) information products.

  • Registry and Search Catalog generation has utilized STAC

  • Processing has utilized Jupyter Notebooks

  • Storage formats included the transition of imagery to formats that support cloud-native geospatial processing, e.g., Cloud Optimized GeoTIFF (COG) and GeoPackage

  • Storage locations such as Amazon Simple Storage Service (S3).

    • Visualization:

  • Mobile client applications able to discover, request, and download DRI products as GeoPackages in support of disaster response field personnel, operations, and decision making during connected-disconnected operations

  • Web/desktop client applications able to interact with cloud-based server components to access both ARD and DRI products for analysis and visualization by analysts and decision-makers

  • Delivery standards for these clients include GeoRSS, Web Coverage Service (WCS), Web Feature Service (WFS), and Web Map Tile Service (WMTS)

  • Platforms to serve the geospatial data and visualize it, e.g., GeoServer and GeoNode, with transmission standards such as Web Coverage Service (WCS), Web Feature Service (WFS), and Web Map Tile Service (WMTS)

Having these pre-agreed and understood in advance can ensure consistency and an efficient processing/delivery of the needed disaster-related information.

6.2.  Step 2: Develop Dataset Recipes or Tools to support the access, sharing, use, reuse, and exploitation of geospatial data

To be part of the OGC Disaster Pilot ecosystem, providers need to develop a value chain as shown in Figure 7, whether this is a non-proprietary tool to help access, share, or visualize data, or the development of an indicator recipe to create either an ARD or a DRI value chain that produces a dataset that can be used directly, or combined with other information in a GIS.

Examples of the tools and indicators developed by Pilot participants can be seen in the Annexes to this Guide, with details of the indicator recipes, collaboration possibilities, and persistent demonstrators.

A number of tools can be used to implement these recipes in a way that is readily interchangeable and reusable in different contexts. One approach is the use of open-source web-based scripted tools like Jupyter Notebooks, often involving Python and other languages such as Java or R. Another approach is to use model-based spatial Extract, Transform, and Load (ETL) tools to support data integration and automation. Either approach can support rapid recipe development to generate the data products necessary to support disaster responders, and examples of both approaches were tested in the context of Disaster Pilot activities.

An example of working through the process of understanding what is needed were the discussions within the ARD and DRI working groups in Disaster Pilot 21, with the OpenStreetMap (OSM) conversion undertaken by Safe Software using FME as follows.

  • Areas of interest extents or polygons were shared in GeoJSON format allowing all participants to be sure discussions were about the same geographical extent.

  • Basemap data was extracted from OSM and shared via a GeoPackage as foundation layers. Although this may sound like a trivial exercise, it was not because:

    • there was an interpretation process when extracting information from OSM and ‘flattening’ it for use in a GIS; and

    • a further complexity was that the original OSM data use geodetic coordinates and the Coordinate Reference System (CRS) ‘EPSG:4326’, where latitude is specified before longitude, while a GeoPackage defines co-ordinates according to the OGC Well-Known Text (WKT) standard of x,y,z,t that will override any CRS axis order, i.e., longitude would come first.

In DP23, a similar focus was placed on the climate projections from models that are often distributed in formats such as NetCDF (Network Common Data Form), a community standard for sharing scientific data. NetCDF has advantages such as being self-describing and appendable with climate community agreed standards such as the CF Metadata Conventions. However, it can be difficult to use/understand alongside data being stored in large files. One solution is to create virtual Zarr interfaces (a format designed for cloud storage and access) while another is to just extract the data of interest and reformat the data into a simpler format, e.g., serving point/polygon data via a Features server.

6.3.  Step 3: 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 half is getting the outputs of that work to the people managing the disaster response, including the field responders on the ground via their mobile phones or similar devices.

There are a variety of solutions for this and so the Pilots do not recommend one, nor do the Pilots suggest that the solution would be based around a single technology. Instead, using a set of standards for data sharing, as described in Clause 6.1 will enable data to be interoperable and reusable across any platform. Solutions could be provided that are open source, commercial, or even using existing internal infrastructures.

The key element is that the receiving organization has a solution where the decision-ready indicators can be uploaded for users to access. The preferred solution will depend on the organization’s infrastructure, financial pressures, technical skills, etc.

Within the Pilots, several external platforms were tested, including the following.

  • Immersive Indicator Visualizations

Immersive Indicator Visualizations were developed by USGS-GeoPathway which offers ARD and DRI to enhance comprehension of drought and wildfire management across varied spatial-temporal scales. It has two elements: Disaster Augmented Reality Simulation Table (DARSIM) — DARSIM modernizes traditional simulation tables, replacing bulky sand models with a portable, data-integrated solution, designed in response to wildland firefighter needs; and Single Pane of Glass (SPoG) — The SPoG provides a unified view of multiple data sources, promoting synchronized decision-making using DRIs and ARD.

  • Geocolloborate

Geocolloborate is a platform developed by StormCenter Communications under the U.S. Federal SBIR program, which offers an option for an expert to lead the analysis and sharing of trusted data with a series of followers receiving the data in real-time on the same screen. This approach offers the potential for a lot of people to interact with the same information at the same time leading to collaborative decision-making with the latest data available, some of which could be updated in real-time.

  • GeoNode Platform

GeoNode, developed by GeoSolutions, is a web-based application and GIS platform for displaying spatial information. A GeoNode controlled by HSR.health has been used to display various data layers that were then accessible using open standards.

If an external platform is chosen, it is important to ensure that it can comply and adhere to the Standards highlighted in Clause 6.1. In addition, it will be necessary to ensure the following.

  • Licenses have been agreed with the external provider for the use of the platform, including sufficient licenses being available for everyone who might need access to data during a disaster.

  • All possible users have and know any username and passwords required to access the external system. In addition, this could also include additional security to allow only certain users to see specific datasets — this approach was tested through encrypted GeoPackages.

  • All possible users have received training in the use of the system for disasters.

It is acknowledged that similar points will be relevant to in-house solutions. The key element is that the chosen platform itself should support the data standards which will be used by the data providers to ensure that the indicator and data sets will be portable between platforms.

6.4.  Step 4: Operationalize the Disaster Response Tool or Workflow

Step two focused on developing datasets workflows, applications, and visualization tools, but to prevent these tools from being proof-of-concept examples or a short-lived demonstration, the tools need to be able to be operationalized to promote the wider understanding of how geospatial data can support emergency and disaster communities.

Providers need to maintain persistent demonstrators of applicable data workflows or tools, and offer disaster and emergency communities the opportunity to test workflows and tools in controlled environments to experience how the workflows and tools work. In the Annexes to this Guide are descriptions of all the data workflows and tools which should give an understanding about what the provider is offering, but it is the use of the tool or workflow in practice or within disaster exercises that will determine whether it works for that community, whether changes are needed, or whether it is a success.

Finally, it will be important for the users of the datasets, indicators, and tools to understand what the impact will be, and what specific decision trees will be enacted when an indicator reaches a certain level. For example, in a flood or wildfire situation, at what point is an evacuation order issued? This will be necessary to give the decision-makers confidence in data-driven decisions and knowing how they should respond.

In summary, the workflows and tools need to be established for the long term, not just for the project, otherwise there is no point in any of the disaster and emergency communities testing and using workflows and tools, if the communities cannot be sure the workflows and tools will be available when a disaster situation strikes.

Although not developed within the Pilots, one suggestion was to develop a series of Operational Readiness Levels (ORLs), similar to those developed by Earth Science Information Partners (ESIP) for making Earth science data more trusted, which will identify the steps and operating standards that both data providers and users will need to take to be able to fully participate.

7.  Descriptions of Case Study Areas and Hazards

The vision of the Disaster Pilot 2023 (DP23) initiative revolved around bringing the technological pieces together and increasing stakeholder engagement, in order to reduce the preparation time and accelerate the ability to transform data from observations into decisions. To achieve this goal required bridging the divides between providers, responders, and other stakeholders, forming a connected ecosystem of data and technologies, and developing the capacity to produce Decision Ready Indicator (DRI) products that answer decision makers’ questions almost as fast as the questions can be posed.

Previous work delineated multiple phases of cycles of activity within disaster management, see Figure 4, all of which depend on getting the right information to the right people at the right time.