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.

5.2.1.  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.

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.

6.1.1.  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.

7.1.1.  Manitoba: Drought Hazards

Manitoba in Canada has been used in DP23 as the case study area looking at hazards associated with drought. Specifically, the area covered is the provincial boundary of Manitoba, covering an area from 49 to 52 degrees North.

Canada’s Changing Climate Report projected a substantial increase in the number of hot days for the region, highlighting the potential increased risk of droughts which will affect many aspects of Manitoba’s landscape. Droughts not only affect agriculture. Water-sensitive areas, such as power generation, fisheries, forestry, drinking water supplies, wildfires, manufacturing, recreation, wildlife, and aquatic ecosystems, can also be severely affected due to recurring droughts (Manitoba Green Plan, 2020).

For example, in Manitoba, from 1988 to 1990 and 2002 to 2003, drought in the Churchill/Nelson River Basin reduced agricultural production to 60% below average, caused an $80M CAD loss in reduced hydropower exports, a massive loss of wetland habitat, and an increased incidence of disease (Manitoba Green Plan, 2020). A 2012 drought caused wildfires to break out near the communities of Badger and Vita, leading to the declaration of local states of emergency. Manitoba’s recent extreme drought of 2021-22 throughout most agricultural regions reduced hydropower exports by $400M CAD (Manitoba Hydro, 2021) and decreased crop yield by 37%, equivalent to an estimated $100M CAD in revenue, and caused the loss of an estimated 270 jobs.

The individual workflows for this Case Study can be found in Annex B.

7.1.2.  Southwestern United States: Wildfire hazards

There are three southwestern states selected as the case study areas for wildland fires.

• Utah

  • Fish Lake with a 50-mile radius.

    Fish Lake is a high alpine lake in south-central Utah, which lies within the Fishlake National Forest. The lake is five miles long and one mile wide and the surrounding forest covers 1.5 million acres. The forest is home to Pando, a huge cluster of 40,000 aspen trees covering over 100 acres, that all share the same root system. Fishlake National Forest was the site of Utah’s largest wildfire of 2023, which began with a lightning strike and burnt almost 8,000 acres during August.

  • Brian Head with a 25-mile radius.

    Brian Head is a small town in Iron County, Utah, with a population of around 100 people. It sits at 3,000 meters above sea level and is the highest elevation town in Utah. Given the town’s altitude, it has an alpine climate with cold winters and annual snowfall of around 9 meters, while the summer provides frequent thunder storms from the monsoons. Brian Head sits within the Dixie National Forest, which covers almost 2 million acres. The vegetation in the Dixie National Forest ranges from desert-type plants at lower altitudes, through to pine and juniper, and finally aspen and coniferous trees at the higher altitudes.

  • Parley’s Summit with a 25-mile radius.

    Parley’s Summit is a mountain pass at the top of Parley’s Canyon, to the east of Salt Lake City in Utah. The summit has an elevation of 2,170 meters and is the highest elevation point of the I-80 highway in the state. In 2021, a wildfire burned almost 550 acres in the Canyon and led to thousands of evacuations.

• Arizona

  • Tucson with a 25-mile radius.

    Tucson is the second largest city in Arizona and has a population of over a half a million people. It is situated between Saguaro National Park to the east and west, and the Coronado National Forest to north and south. Tucson is surrounded by five minor mountain ranges and has a hot desert climate, with the summer average daily high temperatures between 98 and 102°F. The wildland fire risk in Tucson, on the most dangerous fire weather days, is very high and expected to increase with climate change.

  • Sedona with a 50-mile radius.

    Sedona is a small city in the north of Arizona, within the Verde Valley region. The city sits within the boundaries of the Coconino National Forest and borders four wilderness areas and two state parks. Sedona is surrounded by 1.8 million acres of mostly coniferous woodland and has a semi-arid climate with mild winters and hot summers where the average temperatures approach 100°F during July. Increasing temperatures coupled with low levels of precipitation mean the forest is ideal fuel for any wildfires. In early 2023 lightning caused the Miller Fire, which covered around 30 acres before being bought under control.

• California

  • South Lake Tahoe with a 25-mile radius.

    South Lake Tahoe is the most populated city in El Dorado County, California, and is based within the Lake Tahoe basin. The city itself covers a total area of 43 square kilometers and lies along the southern edge of Lake Tahoe in the Sierra Nevada mountains surrounded by forest wilderness areas. South Lake Tahoe has a climate featuring chilly winters, and summers with warm to hot days and cool nights with very low humidity. The city can have temperatures reaching up to 90°F in July and August. The June 2007 Angora Wildfire was the worst forest fire in Lake Tahoe history, which burned more than 3,000 acres destroying more than 250 homes and a large area of forest. Since then, the Tahoe Fire and Fuels Team has treated tens of thousands of acres of forest around Lake Tahoe and is using forest management to reduce the threat of catastrophic wildfires. In 2021, the Caldor Fire went around the populated areas due to the treated forest and firefighting effort.

The individual workflows for this Case Study can be found in Annex C.

7.2.1.  Rimac and Piura Rivers: Landslide & Flooding Hazards

Peru’s Piura region in the north and the Rimac river basin near Lima are both impacted by difficult to predict El Niño related flooding. The El Niño/Southern Oscillation (ENSO) is a naturally occurring phenomenon in the tropical Pacific coupled ocean-atmosphere system that alternates between warm and cold phases called El Niño and La Nina, respectively.

The Piura climate is arid but can experience very heavy rainfall associated with the high nearby Sea Surface Temperature (SST) during El Niño phases. When heavy rain occurs, severe floods can occur, which in turn can cause mudslides called huaycos. Figure 5 shows an index that indicates the El Niño phases in red and La Nina phases in blue.

Figure 5 — ENSO index with red indicating El Niño periods, Multivariate ENSO Index Version 2 courtesy of NOAA, USA

As an example of the relationship between ENSO and flooding, El Niño brought rains that caused severe flooding in 1982-1983 and again in 1998 but then, for several years, droughts and extreme heat were the main worries for these communities. Then the flooding returned again in 2002-2003 and 2017-2019. In 2017, ten times the usual amount of rain fell on Peru’s coast, swelling rivers, which caused widespread flooding and triggered huge landslides that tore through communities (Collyns, 2017).

The individual workflows for this Case Study can be found in Annex D.1.

7.2.2.  Red River Basin: Flooding hazards

One of the most common types of flooding is river flooding, where the river (or rivers) overflow due to high rainfall or rapid melt upstream that causes the river to expand beyond its banks. The Red River flows north from Northeast South Dakota and West Central Minnesota into Manitoba Canada, and eventually out into Hudson Bay. The relatively flat slope of the Red River valley means that the river flow is slow, allowing water runoff from the land to backfill into tributaries, particularly when the downstream river channel remains frozen. In addition, localized ice jams may impede the water flow, resulting in higher river levels.

Therefore, conditions that determine the magnitude of a spring flood include (Anatomy of a Red River Spring Flood):

1. the freeze/melt cycle;

2. early spring rains increase melting of the snow pack or late spring snow storms add to the existing snowpack;

3. the actual snow pack depth and water equivalency;

4. frost depth;

5. ground soil moisture content; and

6. river ice conditions.

A typical spring thaw occurs from the middle of March across southern portions of the basin and mid or late April across the north.

An unusually wet fall and winter, combined with spring melting, drove the water levels up in April 2020. Figure 6 shows the April 2020/2021 water level comparison for the City of Winnipeg’s main gauge (James Avenue).

Figure 6 — Red River water level April 2020/2021 comparison, Winnipeg river levels

Winnipeg does have a 48 km floodway (long excavated channel) to reduce flooding within the city, but the floodway can only be opened when there are no ice jams. The floodway successfully protected Winnipeg from flooding during the high-water levels of 2020, and it is estimated the floodway resulted in around 930 million m3 of water being diverted around the City of Winnipeg (Manitoba, 2020 — PDF).

Unfortunately, ice jam events impacting the lower reaches of the Red River, between Winnipeg and Lake Winnipeg, have increased in both severity and frequency over the last century, a trend which is expected to continue and worsen in the future (Lindenschmidt, 2010).

The individual workflows for this Case Study can be found in Annex D.2.

7.2.3.  Integration of Health and Earth Observation (EO) data and services for pandemic response in Louisiana in the United States

The State of Louisiana is located on the coast of the Gulf of Mexico, between Texas and Mississippi and covers a geographical area of just over 43,000 square miles. Louisiana is divided into 64 individual parishes and was estimated to be home to over 4,600,000 people in 2019 (US Census, 2019). Almost 16% of the population are over the age of 65, and just over 23% of the population are under the age of 18. Like the rest of the planet, Louisiana has suffered with COVID-19. By the middle of October 2021, over 750,000 cases of COVID-19 had been confirmed in the State, with over 14,000 deaths reported to date.

The climate within Louisiana is considered to be subtropical, and the physical geography of the area includes the Mississippi floodplain, coastal marshes, Red River Valley, terraces, and hills. Louisiana’s largest city, New Orleans, lying five feet below sea levels and protected by natural levees, is prone to flooding and hurricanes. During the pandemic, the area was hit by its second-most damaging and intense Hurricane, Ida, the most damaging being 2005’s Hurricane Katrina that flooded 80% of New Orleans.

The individual workflows for this Case Study can be found in Annex E.

7.2.4.  Technical Architecture for Disaster Pilot 21

The technical solution aimed to bring together multiple providers into an ecosystem that transformed input data into actionable information by developing prototypical components and services. These components aimed to utilize modern cloud architectures and next-generation technologies to optimize collaborative workflows and scale solutions rapidly. An overview of the architecture for the Pilot is shown in Figure 7.

Figure 7 — Pilot architecture overview

At the top of Figure 7 are the data sources, which included Earth Observation (EO) and social-political-economic data alongside ancillary geospatial sources. The sources of data were seen as both being free-to-access and paid-for, and the availability could be restricted depending on the source and sensitivity of the data.

Sources considered or used within the Pilot included the following.

• Satellite Earth Observation

  • _Copernicus Sentinel Missions is operated by the European Union, with data acquired by constellations of global missions focused on specific technologies. For example, Copernicus Sentinel-1 are two Radar missions (A&B) that operate at the C-band frequency. Since the Pilot, Sentinel-1B was lost.

  • _Landsat-8 is a high resolution (30 m spatial resolution) mission that carries the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments. Landsat 9 is the most recently launched Landsat satellite, but was not launched in time for the Pilot.

  • _NewSat was a Satellogic constellation that consisted of 17 commercial NewSat satellites in sun-synchronous Low Earth Orbit (LEO).

  • _PeruSat-1 is a National Commission for Aerospace Research and Development’s (CONIDA) small (~ 430 kg) satellite launched in September 2016 which carries NAOMI (New AstroSat Optical Modular Instrument) that has a panchromatic band and four multispectral wavebands: Blue, green, red, and Near-InfraRed (NIR). The panchromatic band has a spatial resolution of 0.7 at nadir, while the multispectral bands have a spatial resolution of 2.5 m at nadir.

  • _RADARSAT includes three RCM (RADARSAT Constellation Mission) satellites that operate at the C-band frequency, with a spatial resolution from 1 to 100 m depending on the acquisition mode.

• Health data

  • Tracking

    • Locations of interest, e.g., specific and black markets.

    • Large gatherings

    • People and items going into and out of any facility to access disease spread risk.

    • Land Use Change, e.g., change in air quality, coastline, the water table, access to water, deforestation, animal, insect, human habitats, and many others.

    • Migration of Animals and Insects

  • Measuring

    • Point-in-time population density

  • Monitoring and monitoring

    • Evacuation routes and development of alternate evacuation routes

    • Health resource utilization within a medical facility.

    • Volume of traffic at discrete points in transportation infrastructure, e.g., bridges, key intersections, train/light rail stations, etc.

  • Assess distance, travel time between population centers & medical facilities (based on traffic, land cover, storm, etc.).

• Social-political-economic

  • Administrative boundaries

  • Census micro data

• Ancillary geospatial

  • OpenStreetMap (OSM): Buildings and road network.

  • Meteorological Data: For example, storm tracking alongside information on weather conditions such as precipitation and wind speed.

  • Digital Elevation Model (DEM): Shuttle Radar Topography Mission (SRTM) @ 30 m spatial resolution provides global coverage and several enhanced DEM versions thereof (e.g., http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/), while airborne lidar data can provide sub-meter resolution locally.

  • Other in situ observations such as stream gauge data.

The input data, ideally in an ARD format, were ingested into cloud-based storage areas from where the data could be transformed into DRI. Users interacted through Mobile and Web/Desktop Applications (Apps), shown in the boxes at the bottom, which interacted with the DRI through a Registry and Search Catalog and GeoPackages. In addition, users provided Volunteered Geographic Information (VGI) into the overall ecosystem to support real-time insights into what was happening.

A key output was recognition and acknowledgment of the need to address:

• stakeholder collaboration on data-to-decision workflows,

• readiness, resilience, and timeliness of data collection and processing to support critical disaster management decisions;

• flexible and scalable deployment of workflows and applications necessary to support disaster practitioners in their day-to-day and minute-to-minute responsibilities; and

• publication and visualization tools to promote a broader understanding of the wide range of scales in both geography and time over which coordinated actions are needed for disaster resilience.

9.1.1.  Recommendations

• Increase the involvement of first responder and emergency manager stakeholder community before the start of OGC Pilots to help define themes and deliverables to ensure the Pilots meet the stakeholder community’s wants and needs.

• Ensure Pilots retain work to push the boundaries of the use geospatial data in the areas the users require help and support.

9.2.1.  Recommendations

• Include AI/ML activities as part of future Disaster Pilots, including solution development, training data sets, and an assessment and comparison of solutions.

9.3.1.  Recommendations

• Working with first responders and emergency managers in the stakeholder community to identify quick wins for geospatial data and ensure these form part of the deliverables for future Pilots

• Encourage the inclusion of real time, sensor-based indicators, and/or dashboards in future Pilots

• Promote the use of OGC SWE — sensor web enablement standards for sensor communications and OGC APIs for the publication of indicators

• Continue working on the specific elements of the Operational Capacity Guide to provide further support to help disaster and emergency communities to improve their geospatial readiness

9.4.1.  Recommendations

• Ensure some type of climate services or variables are made available early in the Pilot to participants building indicators and data workflows to promote future climate projections becoming part of the outputs alongside the past/present/real-time perspective. Ideally, future looking indicators should support multiple scenarios, so that a variety of climate scenarios can be assessed.

• Ensure climate services are available via open standards such as OGC APIs.

• Further development of the concept of ARD in terms of climate services. ARD in terms of observations or measurements is well understood, but it is less defined for forecasting and future looking datasets due to the various potential scenarios available. Making climate ARD more easily understandable, accessible, and usable for disaster and emergency planners and managers will be critical in developing datasets and indicators that will offer future benefits.

• Developing climate DRI’s will also benefit from more engagement with local and domain experts to help interpret and visualize potential climate impacts, including better engagement with indigenous peoples who, given their traditional knowledge, are often the leading local experts in terms of what local resources are most valuable and potentially vulnerable. This will be particularly important in terms of helping define the business rules of the indicators, i.e., if flooding is getting more severe, by how much does the timing of the evacuation order need to be altered. The challenge will be to ensure that indicators are not developed in such a way that the indicators are so locally tuned to only be valuable in one specific area.

9.5.1.  Recommendations

• Establish a long term persistent OGC disaster ecosystem where current, past, and future geospatial data flows and tools to support disaster planning and response can be made easily available and accessible to communities and organizations who might want to operationalize them.

• Explore how persistent demonstrators can provide examples of how to use already available services, including the use of indicator displays and visualizations.

• Involve the OGC Disaster Pilots in a greater manner in supporting the development and implementation of OGC Standards.