Maps are an important means of representing data across physical space. For millennia, humans have made maps to convey geographic information such as the location and extent of rivers, mountains, dwellings, and more. In modern times, the availability of geographic information systems (GIS) has greatly expanded the use of maps to include spatial representations and analysis of demographics, points of interest, and other datasets with a location component.

But while locating places and objects on Earth’s surface is what people usually associate with mapping, another critical use of maps is recording changes over time and space. Commonly referred to as change detection, this mapping capability provides insight into how landscapes, people, and infrastructure evolve, producing spatial patterns that would be difficult to glean without visualizing these changes on a map.

What is change detection mapping?

Change detection mapping refers to spatially analyzing and recording features that have evolved in a particular place over a specific period of time. Rather than placing two maps of the same location from different times side-by-side to observe any differences, true change detection highlights which exact features have changed in the area of interest over the given time period.

Recording change in this way streamlines analysis, meaning geospatial professionals do not need to analyze every single data feature to understand what has evolved over time. Instead, features that have experienced change are highlighted either through field attribution or as a separate data layer that can be loaded into a GIS for visualization and further analytics. Change detection mapping makes it easy to quantify, locate, and describe what exactly has changed over time, which can inform a wide variety of analysis use cases.

Change detection mapping examples

Everything in the world changes over time, especially in today’s dynamic society. With a changing climate, migrating population, and many rapidly developing communities, change detection mapping is essential for keeping geospatial data and analytics up-to-date. 

While virtually any feature can be mapped using change detection methodology, at Ecopia AI we see the highest demand for change mapping with the following features.

Building footprints

Buildings are among the most frequently changing elements of our world. According to the US Census Bureau, 1.7M+ new residential buildings were completed in February of 2024 alone. That’s to say nothing of commercial, industrial, government, or other building types or the construction taking place around the world. Detecting building change is also critical when considering additions, demolitions, and other alterations that can impact a property or community landscape. Building change detection provides useful insights for property risk analytics, tax assessment, population estimation, and urban sprawl measurement.

Impervious surfaces

Rapid urbanization and development not only result in building change, but also an expansion of impervious surfaces in an area. This is because as communities develop, more roads, buildings, parking lots, sidewalks, and other impervious surfaces replace natural land cover features that absorb water. Understanding how impervious surface coverage has changed over time is a crucial part of land use planning and analysis, particularly when it comes to flood resilience. 

Land cover

Detecting change across all land cover types is also important. Monitoring how land cover such as tree canopy, grass, shrubbery, bareland, and other natural features changes over time is helpful for understanding deforestation, pollution, urban heat, and similar climate issues. It’s not only the removal of natural land features that makes land cover change detection popular, but also the change in the distribution of land cover throughout an area. For example, a new parking  lot located next to a wetland may impact its health, while newly planted trees can result in less stormwater runoff or sun exposure.

Transportation features

Even the highly detailed sub-classifications of individual land cover features are often mapped to monitor change. While knowing where a road surface has changed is helpful, many transportation planners need to know how individual lanes or intersections have changed over time in order to analyze networks for safety, accessibility, and sustainability. Similarly, detecting changes in sidewalk or crosswalk width and visibility is used to see how the safety and accessibility of pedestrian features are either improving or declining.

Top 5 change detection mapping applications

Change detection is a frequently used mapping technique across industries, but is especially leveraged for the following use cases.

Insurance risk assessment

Property and casualty (P&C) insurance carriers must analyze the risk profiles of properties in their book of business not only to price policies effectively, but also to define their reinsurance strategy and accurately respond to claims. Detecting change in properties is particularly important at the time of policy renewal or claims response, as an addition of a new room or the construction of a new building next door could impact a property’s risk profile and subsequent pricing or payout.

See how Flow Flood Insurance uses Ecopia building footprint data to detect year-over-year property change and provide better coverage for policyholders.

Stormwater management

As communities develop and impervious surfaces expand, municipalities must be cognizant of stormwater implications. Impervious surfaces lead to higher levels of runoff that contribute to flooding, which can be further exacerbated by the increasing intensity and frequency of stormwater events. Stormwater management professionals are increasingly leveraging change detection mapping to understand how the distribution of impervious surfaces is impacting community flood risk and stress on drainage infrastructure. 

Learn how the City of Detroit used change detection to discover a citywide 2% YoY increase in impervious surfaces, amounting to $5.6M+ in stormwater utility fee (SUF) discrepancies.

Climate resilience

There are many other climate-related applications for change detection mapping beyond stormwater, including natural resource management, flood modeling, green infrastructure planning, and more. Land use changes can dramatically affect the climate resilience of a community, leading to polluted waterways, endangered wildlife, urban heat islands, and similar impacts.

Read how Ecopia’s work with NOAA is empowering coastal US states with the high-resolution land cover data needed to effectively monitor land use change.

Transportation safety

Land use change also has implications for transportation planning. Not only is it important for planners to understand how population migration impacts transportation network demand, or how traffic and pedestrian features change over time, but monitoring changes in surrounding land cover is also critical to ensure infrastructure is sound. For instance, an increase in impervious surface adjacent to a road may result in a higher likelihood of flooding, putting motorists at risk.

See how Network Rail uses change detection mapping to monitor the impact of land use change on the UK’s rail infrastructure safety.

Property tax assessment

Change detection is also frequently used by tax assessors to streamline their property analytics and ensure rates are assigned fairly. With comprehensive, accurate, and up-to-date building footprint data that includes both main structures and outbuildings, tax assessors can reduce the number of onsite visits needed and also add unregistered structures to their records.

Learn how Sutter County, CA fairly assesses property taxes with Ecopia building footprint data.

How to map change over time

There’s no denying that change detection is an incredibly useful mapping application for many industries. However, not all change detection methods are simple to perform. While traditional change detection mapping was a heavily manual process, advancements in AI have unlocked new capabilities that have greatly increased the efficiency of identifying and recording geospatial feature change over time.

Generally speaking, there are three ways to perform change detection mapping:

Manual imagery analysis

The most basic way to detect change from maps is to compare two geospatial images of the same location from different times. From there, analysts can manually note what has changed, whether on the map itself or in another record-keeping system. 

Manual digitization

Taking it a step further, many geospatial professionals will use the imagery to digitize what has changed into a data layer itself. While manual digitization is time-consuming and resource-intensive, especially over a large geographic area or when comparing multiple time periods, it provides a tangible dataset that can be used for further analysis.

AI-powered mapping

Thankfully, innovations in geospatial AI have largely automated the change detection process without sacrificing the data quality expected of a trained GIS professional. AI-based feature extraction not only can efficiently digitize buildings, impervious surfaces, land cover, transportation infrastructure, and any other feature discernible to the human eye, but also compare two map images and extract only the features that have changed. This saves time and resources while also enabling organizations to scale their change detection mapping across larger areas and time periods for deeper insights.

Mapping change detection with AI

Ecopia AI frequently works with organizations across industries and around the world on AI-powered change detection. Whether you have your own imagery or need us to source it from our list of global partners, we can help your organization efficiently extract vector layers that represent changes in whatever features you need to analyze. 

To learn more and kick off your change protection project, get in touch with our team.