Why You Should Stop Manually Digitizing Impervious Surface Data

A comprehensive, up-to-date, and accurate impervious surface database is an invaluable resource for communities to support flood modeling, climate resilience strategies, stormwater infrastructure planning, and more. Though it has historically been time-consuming and expensive for municipalities to collect and maintain high-precision land cover data, advancements in technology are making this data more accessible and cost-effective than ever before.

This blog explores the difference between building and buying an impervious surface database, and specifically, how Ecopia’s AI-based technology is helping municipalities access the high-precision impervious surface data needed to support their communities.

The importance of impervious surface data

Land cover data is generally divided into impervious and pervious categories based on the capacity to absorb or convey water. Pervious surfaces are surfaces that allow water to pass through such as soil, grass, and gravel. Impervious surfaces are typically artificial surfaces that do not absorb water or allow water to seep into the ground. Examples of impervious surfaces include sidewalks, roads, pavement, and buildings. 

Impervious surfaces can adversely affect the environment in various ways. By preventing the absorption of water into the ground, impervious surfaces can cause increased stormwater runoff contributing to flooding, erosion, and the deterioration of water quality. Impervious surfaces can also contribute to the urban heat island effect by absorbing and retaining heat, leading to increased temperatures in urban areas, which is a significant contributor to weather-related fatalities in the US.

Examining impervious and pervious surface data can help municipalities better understand their impact on communities and guide decision-making related to stormwater mapping, flood modeling, stormwater utility fee (SUF) assessment, and more. For example, impervious surface land cover layers are important to help municipalities determine runoff coefficients and calculate SUFs for properties. However, as valuable as impervious surface data is, it can be difficult to acquire and maintain. 

Building an impervious surface database

When it comes to creating an impervious surface database, municipalities’ options typically fall into distinct categories: constructing an impervious surface database or purchasing one. Before determining where to source impervious surface data, it is important to thoroughly consider its intended use, and how much effort will be required to maintain the data for that purpose. Whether a municipality plans to use impervious surface data to create equitable SUFs or to establish flood models to promote community safety, it is important for the data to be as accurate and fresh as possible to ensure effectiveness.

First-party collection and manual digitization

A municipality might decide to collect impervious surface and other land cover data through field surveys, opting for on-site inspections and data collection by field teams. This collected field data is then processed, digitized, and integrated into the municipality's impervious surface database. While direct, this approach can be incredibly time-consuming and expensive. It is important to note that impervious surface databases need regular updates to reflect changes that happen in the urban landscape like new developments or demolitions. This means that municipalities will need protocols for ongoing data maintenance and, if using this method, would need to conduct periodic surveys to keep the database current.

Municipalities may also collect impervious surface data digitally using geospatial imagery, either capturing aerial imagery themselves or using a provider. After receiving the imagery, the next step involves manually digitizing features into classified vector layers, which can be an expensive and time-consuming process, especially to get the level of detail needed for thorough analysis. For instance, it took the GIS team in Collier County, Florida four years to just digitize driveways, not including any other features that would be necessary for analysis. This can also be an incredibly tedious process. For example, differentiating between elements such as a sidewalk and tree canopy overhang during the digitization process can be challenging. Of course, data manually digitized from geospatial imagery also becomes stale over time, meaning this process needs to happen periodically to ensure data freshness. 

Municipalities often choose to source impervious data from open source or publicly available datasets from government agencies, research institutions, or organizations that share information freely for public use. However, this can lead to issues related to quality, accuracy, scalability, and data freshness, as well as licensing concerns in some situations. Municipalities may combine open-source data with data from other sources, but this can lead to non-standardized schemas and create additional complexities when analyzing or updating the data.

Buying an impervious surface database 

Municipalities may purchase impervious data from a provider, offering the advantage of allowing organizations to concentrate their time and resources on analysis rather than investing in manual digitization. However, this depends on the quality and freshness of the purchased data. There are many geospatial data providers that claim to use automation or AI but lack the expertise to do this effectively, which results in inaccurate data. Many encounter challenges when extracting land cover data given the intricacies of surfaces in terms of color, shape, and material. Some providers also use outdated and low-resolution open-source imagery leading to further data inaccuracies. 

There are also firms offering digitization services for municipalities, but this can take just as long and can be even more expensive than doing it in-house due to back-and-forth quality control discussions. 

Ecopia’s AI-powered land cover data

Ecopia’s AI-based mapping systems extract detailed land cover features from geospatial imagery with an unmatched level of accuracy. Ecopia's AI-based mapping systems detect and extract all manmade and natural features captured in our input imagery, and classify the results into high-precision land cover data detailing distinct types of pervious and impervious surfaces. Every year, Ecopia leverages partnerships with leading geospatial imagery providers, updating land cover data from the latest high-resolution imagery to deliver map content that accurately reflects the real world. With Ecopia, municipalities no longer need to choose quality over speed, and can easily access up-to-date impervious surface data for their entire community in a matter of weeks.

An efficient and scalable approach to support stormwater utility fees   

Ecopia’s AI-based mapping eliminates the need for large scale manual digitization, resulting in substantial time and cost savings compared to creating a database from scratch or purchasing one that requires significant cleanup. For example, Ecopia partnered with Detroit Water & Sewerage Department (DWSD) to extract impervious surface data to support their stormwater utility fee (SUF) program. Before partnering with Ecopia, DWSD faced difficulty maintaining an annual impervious surface data layer as it would take 12-18 months after capturing imagery to receive the new data layer. This delay meant that, by the time that data was integrated, the city was working with stale data that did not reflect the true physical world. Ecopia’s solution saved DWDS months of time, providing access to data within only 6 weeks of imagery being received.

Building more accurate flood models with Ecopia land cover data

Similarly, the City of Peterborough faced challenges with its impervious surface database, characterized by inconsistent and outdated land cover data sourced from various sources. Looking for a comprehensive and efficient solution, Peterborough partnered with Ecopia. Ecopia quickly provided a comprehensive, accurate, and up-to-date land cover dataset that helped the city improve its flood resilience and refine its flood modeling capabilities to make informed land use decisions aimed at safeguarding the city from flooding.

Unlocking the potential of high-precision land cover data

By partnering with Ecopia to access comprehensive, accurate, and up-to-date land cover data, municipalities can spend less time on manually digitizing features or cleaning up data and more time on analysis. High-precision land cover data can unlock powerful insights for municipalities, giving them the data they need to identify and solve complex problems related to equity. 

For example, in Houston Texas, Ecopia worked with the Texas Water Development Board and the National Oceanic and Atmospheric Administration (NOAA) to better understand the spatial relationship between income, poverty, tree equity, and impervious surfaces. Examining Ecopia's vector layers with publicly available census data revealed a relationship between income levels and susceptibility to flooding and urban heat effects due to the presence of impervious surfaces. You can read more about this analysis here.

Building a safer and more equitable tomorrow with AI-powered impervious surface data

Last year, the US saw multiple devastating flooding events across the country, including four separate events that caused over a billion dollars in damages each. With the escalating frequency of storms and associated damages, impervious surface data has become increasingly crucial for enhancing climate resilience in communities. Thankfully, advancements in AI-powered technology and federal funding initiatives have made high-precision data more accessible than ever before.

If you're interested in exploring how AI-powered geospatial data can improve your operations, feel free to get in touch for more information.