What is impervious surface mapping?

Impervious surface mapping refers to the geospatial rendering of land cover on Earth’s surface that does not absorb water. While there are many types of land cover throughout the world, they all can be broken down into pervious and impervious surfaces to assist in several environmental use cases.

Impervious vs pervious surfaces

All land cover can be classified as either pervious or impervious. Pervious surfaces refer to permeable land use layers that allow water to percolate through the ground, while impervious land cover layers are solid surfaces that can lead to run off. 

Types of impervious surfaces

Similarly, there are many different types of impervious surfaces throughout the world. For the most part, impervious surfaces are manmade, although there are some examples of nonporous rocks that can be classified as impervious. 

Common examples of impervious surfaces include:

• Roads
• Sidewalks
• Buildings
• Bridges
• Parking lots
• Sports fields
• Swimming pools
• and other manmade structures or features

All types of land classifications have a unique interaction with rainfall based on its properties. Each land feature's contribution to water runoff can be represented by assigning a runoff coefficient. A runoff coefficient is calculated based on the feature’s proportion of water runoff to the amount of precipitation received, which is measured by determining the soil type, gradient, permeability, and land use.

Top applications of impervious surface maps

Impervious surface mapping has always been an important tool for better understanding the environment, but especially so in recent decades. The advent of geographic information systems (GIS) and geospatial data has greatly accelerated innovation in land cover classification and mapping, enabling those studying the permeability of surfaces to derive critical insight and make more informed decisions. 

Mapping impervious surfaces is a key component to understanding and mitigating climate change, which is not only altering the way water interacts with the Earth’s surface, but also increasing the frequency and cost of stormwater events. For example, the cost of flooding in the US is steadily increasing, and predicted to rise from around $32 billion to $43 billion in 2050. Visualizing and analyzing the distribution of impervious and pervious surfaces can help predict, mitigate, and prevent these climate-related weather events. This section breaks down three common use cases for impervious surface data: stormwater mapping, flood modeling, and stormwater utility fee assessment. 

Stormwater mapping

Stormwater mapping utilizes GIS to understand the impact of water from weather events. This involves identifying where all water features, both natural and manmade, are located, as well as mapping and classifying land cover surfaces to determine runoff or overflow risk. Stormwater mapping helps municipalities and planning organizations build and maintain infrastructure to support communities facing a changing climate. 

Common features of stormwater maps include utility and sewer system assets such as storm drains, pipe networks, and catch basins. Mapping these stormwater assets in addition to land cover provides insight into where runoff will occur and how much overflow can be expected given an area’s current infrastructure and climate. Impervious surfaces can be assigned runoff coefficients based on their surface roughness to model how their interaction with stormwater will impact current infrastructure. With up-to-date, high-precision maps of stormwater assets and land cover, municipalities can track land use change and develop infrastructure that boosts climate resilience, reducing the chance and scale of damage from stormwater events.

Flood modeling

Closely related to stormwater mapping, flood modeling enables communities to predict how stormwater will impact an area and prepare for future flooding scenarios. Many municipalities employ hydrologists on their stormwater teams to develop sophisticated flood models that inform planning decisions. However, flood modeling is a specific subset of stormwater mapping with its own unique applications. While municipal stormwater planning departments frequently develop flood models, so do environmentalists, conservationists, meteorologists, and other scientists studying the impacts of climate change. ‍

Flood modeling is a complex science, often managed by engineers or hydrologists who specialize entirely in developing mathematical equations to help predict the possibility and impact of a flood event. Impervious surface data is a critical component of these equations, as are pervious surfaces, elevation, and other elements of the environment. Flood models often include parameters for connected surfaces, meaning analysts can measure how water will interact with multiple surfaces before ultimately reaching the ground (for example, rain falling on a roof before running off onto pavement and then into nearby soil). All of these inputs help flood modelers build equations that take factors like surface roughness, permeability, and slope into account, testing possible flood scenarios to understand the impact.  

Stormwater utility fee assessment

Impervious surface mapping is also leveraged for municipal administration related to stormwater. As the climate changes and municipalities face both rising infrastructure maintenance costs and increased sustainability regulations, over 1,800 US municipalities are implementing stormwater utility fees (SUFs). SUFs are based on the amount of runoff impact a property has, and are typically based on the parcel’s total square footage of impervious surface. This helps fund stormwater maintenance, as well as encourage sustainable development in communities.

To calculate SUFs, municipalities leverage stormwater maps with detailed impervious surface data. Just as flood modelers and stormwater planners calculate surface roughness to understand how much runoff will occur in a climate event, utilities departments determine impervious surface square footage for properties to designate fee rates. A key part of this process is ensuring SUFs are equitably assigned, which requires accurate and up-to-date impervious surface data for calculations.

How to map impervious surfaces

GIS and geospatial data are critical tools for impervious surface mapping, enabling stormwater teams, flood modelers, and utilities departments alike with a digital representation of the environment. The wide variety of analytics tools available through GIS provide efficient and scalable ways to understand impervious surfaces, but collecting and maintaining land cover data is not always so straightforward. This next section highlights a few different ways impervious surface mapping data is curated, ranging in efficiency, accuracy, and scalability. 

Conducting land use surveys

In some communities, stormwater teams conduct on-the-ground surveys of land cover throughout their jurisdiction to develop maps of impervious surfaces. While there are many tools available that quickly load survey data into GIS programs, surveying still requires physically visiting and evaluating each site being mapped. Results of on-the-ground land cover surveys are typically accurate, but risk becoming out-of-date as soon as maintenance or construction occurs. Because surveying entire municipalities on-the-ground is so time-consuming, impervious surface data collected this way should be evaluated for currency before using in critical decision-making.‍

Manual digitization and classification

To ensure quality in impervious surface data, many municipalities manually digitize and classify land cover data from geospatial imagery. While tedious and time-consuming, manual digitization and classification gives stormwater teams increased control over the data they are working with. Teams can choose the freshness and resolution of input imagery used, and apply their own methodology for classifying the land cover layers they need. But digitizing impervious surface features with the level of detail needed for decision-making takes time and resources, especially over large or densely-developed areas. Data can also quickly become stale using this method, as teams often lack the resources to constantly digitize their area of interest. 

Leveraging AI to extract impervious surface layers

Recent advancements in artificial intelligence (AI) have made it possible to scale feature digitization without sacrificing GIS-professional accuracy. Ecopia’s AI-based mapping systems ingest geospatial imagery and extract vector features in a fraction of the time it takes to manually digitize them. The resulting vector layers are classified into different land cover types, representing both impervious and pervious surfaces to enable stormwater mapping, flood modeling, and fee assessment. The scale and efficiency at which Ecopia can digitize impervious surface features means updates can happen much more frequently, ensuring maps and models are always conveying fresh information. In addition to keeping the data up-to-date, Ecopia extracts and classifies features with the accuracy of a GIS-professional, providing geospatial analysts with a source of truth for land cover without time-consuming manual digitization. 

‍Examples of impervious surface mapping with AI

Ecopia has partnered with many different municipalities, government agencies, and engineering firms to map impervious surfaces across a variety of use cases.  This section highlights a few success stories for using AI-based technology to map impervious surfaces.‍

City of Peterborough builds integrated flood model with updated land cover data

Like many communities, the City of Peterborough in Ontario, Canada relies on flood modeling to predict and mitigate damage from climate events. Previous stormwater events have caused tens of millions of dollars in damages to property and infrastructure, leading municipal officials to invest heavily in technology that can help prevent similar scenarios in the future. As part of this strategy, the City of Peterborough partnered with Ecopia to develop detailed, up-to-date, and complete land cover maps of the area. With the GIS-professional quality output, the City’s hydrologists were able to build an integrated flood model that helps them understand connected surfaces, runoff coefficients, and other critical details that determine flooding in the community.

Learn more about the City of Peterborough’s integrated flood model

City of Detroit uncovers an average of $5.6M in annual SUF discrepancies using impervious surface data

‍The City of Detroit, Michigan is also leveraging AI-based systems for impervious surface mapping. To equitably and efficiently determine SUFs for properties across the City, the Water & Sewerage Department has partnered with Ecopia. Ecopia’s land cover feature extraction provides the City of Detroit with an up-to-date, accurate, and comprehensive map of impervious surfaces for fee assessment. The efficiency and scale of Ecopia’s AI-powered mapping help the City detect changes in land cover that affect SUFs, ultimately helping them uncover an average of $5.6M in annual SUF discrepancies and optimize stormwater utility planning. Since 2018, Ecopia has been updating the City’s land cover data annually to help monitor change detection and SUF assessment, and will continue to do so through at least 2026. 

Read about the City of Detroit’s implementation of impervious surface maps for assessing SUFs

Get started with Ecopia’s impervious surface mapping solutions

To learn more about Ecopia’s AI-based systems and how they are increasing accuracy and efficiency in impervious surface mapping, reach out to our team.