Land Cover

Types of Stormwater Mapping Data & Where to Get It

February 9, 2023
Post by
Ecopia AI

What is stormwater mapping?

Stormwater mapping is commonly conducted by municipalities to better understand how climate events will affect their community infrastructure and make planning decisions based on their findings. The increasing availability of higher-resolution geospatial data and mapping tools is enabling more detailed stormwater analysis, helping communities around the world develop climate resilience strategies in a rapidly changing world. Municipalities often employ geographic information systems (GIS) teams and/or hydrologists to manage stormwater mapping and data analysis.

A sample of an impervious surface map used for stormwater management in Detroit, Michigan.

Types of stormwater data

To effectively analyze stormwater events and infrastructure, GIS teams and city hydrologists source, collect, and curate relevant data to map and derive actionable insights from. The most sophisticated stormwater maps are developed from a combination of first- and third-party data that enable municipalities to leverage their own information in addition to complex information validated by experts who specialize in data curation. This section describes common data inputs to stormwater maps and how each is used by municipalities to build climate resilience.

Geospatial imagery

The foundation for many municipal stormwater maps is high-resolution, up-to-date geospatial imagery. Whether used as a basemap or an input to first-party data creation, geospatial imagery helps stormwater mapping teams visualize their area of interest (AOI) on a large scale, and from a perspective not easily achieved otherwise. Up-to-date imagery is especially powerful when analyzed before and after a stormwater event; communities often capture or source imagery post-event to quickly understand the full extent of damage. This not only helps in response efforts, but also enables stormwater management teams to better prepare for future events.

Example of geospatial imagery of Palm Coast, Florida before and after Hurricane Irma in 2017; source: USGS.

Land cover data

While imagery is a helpful input and foundational layer to stormwater mapping, it is not interactive in a way that allows stormwater teams to isolate and analyze individual features. To derive maximum insight from geospatial imagery, GIS teams and hydrologists often create or source land cover data. 

Land cover data digitizes and classifies features seen in imagery so that individual elements can be layered together, or removed, in stormwater maps. For example, in an aerial image a sidewalk may be obscured by tree canopy, preventing analysts from truly understanding the different surface types stormwater will come into contact with. When land cover is represented in its own dataset, stormwater teams can interact with it to feed their flood models, stormwater utility fee calculations, and other use cases that require more insight than images alone provide.

A sample of comprehensive land cover data in Los Angeles, California.

One of the main benefits of comprehensive land cover data is the classification of features as either pervious or impervious surfaces, which facilitates enhanced flood modeling and runoff calculations. 

Impervious surfaces

Impervious surfaces lead to higher levels of runoff due to their inability to absorb water. Common examples include pavement, buildings, and artificial turf. Municipalities leverage impervious surface land cover layers to calculate runoff coefficients and predict how stormwater will interact with their community, as well as calculate stormwater utility fees for each property.

Pervious surfaces

Equally important to understand are pervious surfaces, which do absorb some level of water but also impact the effect of stormwater. Pervious surfaces can include grass, forests, tree canopy, and other natural features that are found in the environment. Stormwater teams combine layers of pervious surface data with impervious layers to understand and model how they affect each other, and inform their climate resilience and planning strategies.

A sample of pervious surfaces (left) and impervious surfaces (right) in Barcelona, Spain.

First-party stormwater data

Each community has its own stormwater infrastructure in place, and often maintains their own data. For example, pipe and sewer networks that are underground and not captured in imagery or land cover data are typically documented and mapped by utilities departments. This first-party data is another critically important ingredient in stormwater mapping and analysis, as it provides further detail about how communities can manage stormwater and complements the insights derived from comprehensive land cover data. 

An example of a flood extent map derived by the City of Peterborough using Ecopia land cover data and first-party data representing the area’s pipe network.

Where to get stormwater map data

There are many ways to source or collect data for stormwater mapping, each with their own pros and cons. This next section breaks down different options for acquiring stormwater map data and provides some examples of where to get started.

First-party data collection

If desired, municipalities can collect and curate their own input data for stormwater mapping. Geospatial imagery can be captured by satellites, airplanes, street-view cars, and even drones. Some municipalities leverage these imagery capture methods to take their own images for mapping and analysis. The benefit of this is that the municipality is in control of when and how the imagery is captured, which can be useful when a stormwater event occurs and commercial providers are not scheduled to capture up-to-date imagery anytime soon. The downside is that, like most first-party data collection, capturing high-resolution imagery can be time-consuming and expensive. Partnering with a provider who specializes in capturing high-quality imagery ensures quality and offloads the effort of capturing an AOI, so many municipalities work with commercial organizations to acquire the imagery they need.

Similarly, land cover data can be collected manually through land surveys. Equipped with handheld GPS devices, GIS teams can traverse their AOI and collect coordinates of features they need. These coordinates can then be uploaded to their GIS database and used in their stormwater mapping workflows. While this gives GIS experts complete control over feature collection, it is extremely time-consuming. Data collected this way also risks becoming stale when inevitable changes occur in the community, as it will only be updated when a field crew goes out to survey again. 

Manual digitization

Land cover data is often collected digitally without conducting land use surveys on the ground. GIS teams frequently use geospatial imagery (whether captured themselves or licensed from an imagery provider) to manually digitize features into classified vector layers. 

Although manual digitization enables stormwater mapping teams to have complete control over the accuracy of these vector layers, it is tedious, time-consuming work. To extract vector features from imagery with the level of detail needed for actionable stormwater insights, GIS analysts must devote hours of dedicated work time to manual digitization. As with data collected during land surveys, data manually digitized from geospatial imagery can easily become out-of-date when changes happen in the real world, simply because of how time-consuming manual digitization is. 

An example of manual feature digitization for one building.

AI-based mapping

Advancements in geospatial technology and artificial intelligence (AI) are revolutionizing the way stormwater map data is produced. Ecopia’s AI-based mapping systems eliminate the need for large scale manual digitization by extracting detailed land cover features from geospatial imagery with the accuracy of a trained GIS professional. Our global partner network ensures we always have the latest, most up-to-date imagery at our fingertips as an input to our feature extraction processes. To give you an idea of the scale and efficiency Ecopia’s AI-based technology can provide, we mapped every building in Sub-Saharan Africa in just eight months - more than 416 million buildings, which experts estimate would have taken 7,500 years to do manually.

An example of land cover data extracted by Ecopia’s AI-based systems for NOAA’s coastal mapping analysis

While other data providers offer feature extraction services and land cover datasets, no other company can match the accuracy, completeness, and freshness of Ecopia’s data. Global governments, nonprofits, and commercial organizations alike rely on Ecopia’s AI-based data as a source of truth for the real world. For example, the City of Jacksonville leverages Ecopia data as the sole-source of its impervious surface maps, and the National Oceanic and Atmospheric Administration (NOAA) uses Ecopia’s land cover as an input into the coastal mapping data they provide to communities across the US.

Ready to get started with accurate, comprehensive, and up-to-date stormwater mapping data? Get in touch with Ecopia’s stormwater team to learn more.

Request a Demo

Related Blog Posts

Subscribe to get our latest posts straight to your inbox!

If you're ready to leverage groundbreaking advancements in artificial intelligence, let's chat. For more examples of Ecopia's extraction capabilities, please view our samples.

Ready to get started?

If you're ready to leverage groundbreaking advancements in artificial intelligence, let's chat. For more examples of Ecopia's extraction capabilities, please view our samples.