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Urban Planning’s New Frontier: The Transformative Power of Satellite Imagery

Editor’s note: This article was written as part of EO Hub – a journalistic collaboration between UP42 and Geoawesome. Created for policymakers, decision-makers, geospatial experts and enthusiasts alike, EO Hub is a key resource for anyone trying to understand how Earth observation is transforming our world. Read more about EO Hub here


Urban planning has undergone a remarkable transformation over the past half-century, driven largely by revolutionary advances in satellite technology and geospatial analysis. What once required months of ground surveys and manual mapping can now be accomplished in days or even hours, with unprecedented accuracy and detail. This technological leap has arrived at a critical moment in human history, as cities worldwide grapple with unprecedented growth, environmental challenges, and the pressing need for sustainable development.

In this article, we’ll explore the transformative role of satellite imagery in urban planning, from its historical foundations to its current applications and future potential.

What Did We Do Before Satellite Imagery?

Before the advent of aerial and satellite technology, urban planners relied heavily on ground-based surveying techniques that were inherently time-consuming and labor-intensive. It was only in the early 20th century when urban planners started to gain access to traditional aerial photography which allowed them to view cities from above. This new perspective was a significant step forward because, for the first time, urban planners could gain real contextual awareness of the terrain, collecting information on land use, agricultural management, forestry, pollution and conservation, wildlife management, and much more. However, it was still quite expensive, offered limited coverage, and suffered from many of the challenges that come when your datasets are fragmented and incomplete.

This made it difficult to integrate different data sources and consider complex planning scenarios – reducing the speed, scale, and comprehensiveness of any development projects. These constraints often resulted in reactive rather than proactive planning approaches, as planners lacked the tools to effectively monitor and predict urban growth patterns. Interventions based on incomplete information have been shown to exaggerate various unexpected externalities that could have been avoided if the right data was more accessible.

In recent years, we’ve seen a lot of progress with platforms like UP42 starting to aggregate these services and resources, enabling them to offer aerial imagery affordably and at a very high resolution, making many of these use cases more accessible for urban planners of all kinds. This has made a significant difference to how urban planners work and it often serves as a gateway to satellite-based imagery which has gone on to fundamentally transform the practice of urban planning, ushering in a new era of data-driven decision-making and comprehensive spatial analysis.

Modern Applications of Satellite Imagery for Urban Planning

Satellite imagery has emerged as an indispensable tool in modern urban planning, offering planners and decision-makers a dynamic, bird’s-eye view of urban landscapes that can be analyzed across multiple temporal and spatial scales. At the forefront of this technological revolution are high-resolution optical satellites such as Pleiades which can capture images with resolutions as fine as 50cm, allowing planners to discern individual trees, cars, and even smaller urban features. This level of detail has transformed how we approach urban analysis, enabling precise measurement of building footprints, accurate assessment of infrastructure conditions, and detailed monitoring of urban development patterns.

However, optical imagery represents just one facet of satellite technology’s contribution to urban planning. The introduction of Synthetic Aperture Radar (SAR) has provided planners with capabilities that would have seemed like science fiction just decades ago. Unlike optical sensors, SAR can penetrate clouds and operate in darkness, offering consistent monitoring capabilities regardless of weather conditions or time of day. This technology has proven invaluable for monitoring urban subsidence, tracking structural deformation, and assessing flood risks – critical capabilities in an era of increasing climate uncertainty.

These technological capabilities have found practical application in countless urban planning initiatives worldwide. Cities are using satellite-derived data to optimize public transportation routes, plan green corridors, and assess the impact of new developments on local ecosystems. The technology has proved particularly valuable in rapidly growing urban areas, where traditional planning methods struggle to keep pace with development.

Another common use case is to monitor urban expansion and its environmental impacts. Planners can track changes in vegetation cover, identify areas of informal settlement, and assess the effectiveness of green space initiatives. This information has proven invaluable for creating more sustainable and resilient urban environments.

Lastly, the integration of these various satellite technologies has led to profound changes in how we approach land use and land cover analysis. Where once planners relied on infrequent surveys and educated guesswork, they can now monitor urban change in near-real-time. This capability has proven particularly valuable in rapidly growing cities, where understanding the pace and pattern of urban expansion is crucial for sustainable development.

All of these applications are enabled not just by the access to high-quality satellite imagery from platforms like UP42, but also by improvements to how this data can be analyzed and applied to urban challenges. By connecting to the UP42 API, urban planners can integrate different data sources, run sophisticated analyses, and validate those results without having to have all the computational power on-site. This drastically improves their ability to scale their work and goes a long way to improving decision-making across a wide range of different metrics.

What Will the Future of Urban Planning Look Like?

The coming decade promises a remarkable evolution in satellite technology that will further redefine our capacity for urban observation and analysis. New constellation systems, comprising hundreds of small satellites working in concert, are set to provide near-continuous monitoring of urban areas. These systems will overcome one of the most significant limitations of current satellite technology: temporal resolution. Instead of capturing images every few days or weeks, these constellations will offer multiple daily passes over urban areas, enabling near-real-time monitoring of urban dynamics.

The advancements in sensor technology have the potential to be equally revolutionary. Next-generation sensors could offer unprecedented spectral and spatial resolution, capable of detecting subtle changes in urban environments that current systems might miss. Hyperspectral sensors with hundreds of spectral bands will provide detailed material composition analysis, while improved thermal sensors will offer precise monitoring of urban heat patterns and energy efficiency at the building level.

However, perhaps the most transformative development lies not in the satellites themselves but in how we process and analyze their data. Artificial intelligence and machine learning algorithms are becoming increasingly sophisticated in their ability to interpret satellite imagery. These systems will be able to automatically detect and classify urban features, monitor changes, and even predict future development patterns with remarkable accuracy. This automation is particularly significant given the enormous volume of data that new satellite systems will generate – and platforms like UP42 are building capabilities to bring that power to anyone who needs it. AI-powered analysis systems will be capable of processing this data in real-time, alerting planners to significant changes or emerging patterns that require attention. For instance, these systems might automatically detect unauthorized construction, monitor traffic patterns, or identify areas of environmental stress before they become critical issues.

usecase-urban-planning

Of course, these technological advances will bring their own challenges. The increasing volume of data will require new approaches to data management and analysis. Privacy concerns will need to be addressed as the resolution and frequency of satellite monitoring increase.

There will also be a growing need for professionals who can bridge the gap between technical expertise and practical urban planning applications. However, if we can thoughtfully integrate them into our existing planning processes and societal norms, the potential is immense. The goal is not to replace human judgment but to enhance it with better data and more sophisticated analysis tools. The cities of the future will be shaped by planners who can effectively combine technological capabilities with human insight and local knowledge.

What Does It Mean for the World?

The future of satellite imagery in urban planning is not just about providing better technology – it’s about creating more livable, sustainable, and resilient cities. As these technologies continue to evolve, they will provide unprecedented opportunities to address urban challenges and create better urban environments for future generations.

Most excitingly, it democratizes access to cutting-edge data that can be used to make holistic decisions. For geospatial professionals, this evolution means moving beyond traditional roles as data collectors and mapmakers to become key strategic partners in urban development. The ability to monitor, analyze, and predict urban changes in near-real-time positions these experts at the forefront of addressing some of humanity’s most pressing challenges.

This technology can be a catalyst for significant positive change if we can leverage it to its full potential. As a community, we’re excited to see how insights will help to manage resource allocation, mitigate climate impacts, and ensure equitable development. When working at their best, these technologies enable evidence-based decisions that can improve millions of lives. And as platforms like UP42 make these tools more accessible, communities and stakeholders can participate more meaningfully in planning processes, armed with the same powerful data as traditional decision-makers.

This technological revolution isn’t just about building better cities – it’s about fostering more inclusive, sustainable, and resilient urban futures for us all. And we can’t wait to see what can be unlocked as the industry continues to grow and evolve.


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5 Ways to Use Pansharpening on Satellite Imagery

Editor’s note: This article was written as part of EO Hub – a journalistic collaboration between UP42 and Geoawesome. Created for policymakers, decision-makers, geospatial experts and enthusiasts alike, EO Hub is a key resource for anyone trying to understand how Earth observation is transforming our world. Read more about EO Hub here


The topic of pansharpening has been covered far and wide. Lots has been said about it, so in this article, we’re going to let the images speak for themselves. But first, here’s a whistlestop tour of pansharpening: what it is, its origins, how it works, and five ways to use it.

What is Pansharpening?

Both well-loved and well-used, pansharpening (short for panchromatic sharpening) is an image fusion technique used to enhance satellite imagery. 

The nature of remote sensing satellites means that they only capture high spatial resolution or high spectral resolution imagery. 

Imagine you only have two photos of the same scene:

  • A very sharp black-and-white photo (panchromatic).
  • A less detailed but colorful photo (multispectral).

That’s where pansharpening comes in. 

It solves two main problems at once, which are:

  1. The panchromatic band has low spectral resolution and high spatial resolution.
  2. Multispectral bands have high spectral resolution and lower spatial resolution.

Pansharpening combines these two images to create a single picture that has the best of both worlds.

The result? A high spatial, high spectral resolution image.

This allows us to see the planet’s features more clearly, making imagery easier for both humans and computers to analyze and understand.

The History of Pansharpening

The roots of pansharpening emerged in the 1970s-1980s as satellites began capturing both panchromatic and multispectral imagery. Early research focused on combining high-resolution panchromatic data with lower-resolution color information. 

Key techniques like IHS transform and Brovey transform were developed in the 1990s, leading to the widespread adoption of pansharpening in the 2000s with high-resolution commercial satellites.

How does Pansharpening Work?

Pansharpening using the Intensity-Hue-Saturation (IHS) method is a popular technique. Here’s how it works:

Step 1: Convert Multispectral Data to IHS Color Space

Multispectral imagery typically includes three bands (e.g. Red, Green, Blue), which have lower resolution compared to the PAN band. The first step to enhancing resolution involves converting the RGB imagery into a new image with PAN-based resolution. Then, this resampled RGB image is converted into an IHS model.

What’s IHS?

  • Intensity (I) represents brightness.
  • Hue (H) corresponds to the dominant wavelength (perceived color).
  • Saturation (S) indicates how vividly these colors appear.

Separating these elements allows us to process spatial information (brightness) and spectral data (color) independently.

Step 2: Merge with a Panchromatic Image

The low-resolution Intensity component is replaced with the high-resolution panchromatic image. By substituting the Intensity component of the multispectral image with a high-resolution panchromatic image, we achieve enhanced spatial detail. The panchromatic image, which has finer spatial resolution than the original intensity layer, effectively boosts the clarity of the final image. The color data—hue and saturation—remains untouched, preserving the spectral richness while upgrading the sharpness.

Step 3: Transform IHS to RGB

Finally, after merging the panchromatic data, the new high-resolution IHS image (with new Intensity) is converted back to RGB. This step restores the color richness from the multispectral data, now combined with the improved spatial detail from the panchromatic layer. 

The result? A visually stunning high-resolution color image that offers both enhanced detail and vibrant, accurate colors.

Did you know you can now pansharpen your scenes directly on the UP42 platform? Provided by Pixel Factory, the algorithm allows you to process high-quality Earth observation products using the Brovey method. 

You can find more information about the requirements, input, and output in UP42’s technical information. Try pansharpening out for yourself on UP42, for a quick and easy way to extract insights at scale.

Pléiades, Airbus, original image

Pléiades, Airbus, pansharpened

How Can We Use Pansharpening?

Now, onto the images. We’ve zoomed into areas around the world – Berlin, Anak Krakatau, Prignitz, State of Pará, and Boxmeer – to give you an idea of how useful pansharpening is across use cases and the globe.

Our workflow looked like this:

  1. Source data: Landsat
  2. Bands: 2 (Blue),3 (Green),4 (Red),8 (Panchromatic)
  3. Tool: GDAL Pansharpening
  4. Aim: Visualize a raster with 30m resolution into a raster with 15m resolution
  5. Locations: 

Berlin, Germany: Urban Areas

First, we looked at Berlin, Germany. It’s an ideal example of an urban area and the impact that pansharpening can have. In the after image, we’re better able to distinguish between closely spaced objects like individual buildings, and roads.

Pansharpening significantly improves the detection of the fine details of urban structures and buildings, which may not be visible in lower-resolution imagery. 

Object detection algorithms perform better when used on pansharpened imagery, as well as other automated analysis techniques. This makes pansharpened imagery particularly valuable for urban planning purposes, such as mapping out population trends, urban land use and land cover changes, and supporting decision-making.

Anak Krakatau, Indonesia: Landslides and Tsunamis

The December 2018 landslide and tsunami in Atak Krakatau, Indonesia drastically reshaped the volcano and surrounding coastal areas. The volcano collapsed into the sea, triggering a devastating tsunami, reducing the island’s height by over 50% and resulting in hundreds of lives lost and casualties. 

By applying pansharpening to satellite imagery of Anak Krakatau, we gain a more detailed picture of the changes. Pansharpened data for natural disasters could lead to better risk assessment, disaster response planning, and understanding of similar events in the future. Also, it’s highly useful for volcano activity monitoring and aftermath events in frequent temporal resolution.

Prignitz, Germany: Green Energy and Farming

Pansharpening satellite imagery of Prignitz, Germany – an area known for its farming fields and wind turbines – can provide significant benefits for green energy applications and related use cases. 

When it comes to planning new wind farms, pansharpening is a game-changer. It’s great for seeing the lay of the land – where the hills are, how close the turbines would be to residential areas, how many wind turbines are there, and what condition they’re in. Images with PAN resolution are perfect for detecting engineering objects and monitoring the construction process, which often requires more pixels for accurate analysis.

In general, pansharpened imagery enhances the ability to detect changes over time. This means better long-term monitoring for both wind farm expansion as well as agricultural development. As you can see in the images of Prignitz, it’s clear how pansharpened satellite imagery helps to keep an eye on the environment.

State of Pará, Brazil: Deforestation

Next, we looked at a critical area of concern for deforestation in the Brazilian Amazon. In 2023, Pará accounted for 36.4% of the total deforestation in the Brazilian Amazon, making it the state with the highest deforestation rate.

As you can see in the images below, there is much clearer visualization in the pansharpened version. This helps with discerning forest boundaries, forest cover changes, and natural forest regrowth. Thanks to this, you can also observe illegal logging more closely – noticing small gaps in the forest cover.

The deforestation rate in Pará has remained high despite overall reductions in Amazon deforestation. For instance, even when Brazil achieved an 84% reduction in Amazon deforestation in 2012 compared to 2004 levels, Pará continued to be a major contributor to forest loss. The enhanced visual interpretation, improved automated analysis through machine learning algorithms, and temporal monitoring that pansharpening offers make it a key step toward tackling deforestation.

 

Boxmeer, Netherlands: Flooding

Boxmeer is a town in upper southeast Netherlands, which experienced flooding during extreme weather across Europe in 2021. We’ve pansharpened imagery of the area, which highlights the difference pansharpening makes.

For flooding, pansharpened satellite imagery can be extremely valuable for monitoring and managing risks, especially in the Netherlands. The country’s historic battle with flooding, combined with climate change and heavy rainfall, means that risk mitigation is key. Over 50% of the Netherlands is below sea level, making the country particularly vulnerable to flooding. Pansharpened satellite imagery can significantly enhance countries such as the Netherlands’ ability to monitor, respond to, and plan for flooding. 

The aftermath of this catastrophic event was focused on a river bank erosion assessment to protect critical places against further potential floods.

Now that we’ve seen what pansharpening can do, it’s incredible to see how far we’ve come with satellite imagery. What began as a clever way to make photos clearer has evolved into a whole field of space tech. 

Today, we’ve got AI and advanced machine and deep learning approaches working their magic to find new ways to improve image quality and reduce spectral distortions. The future of Earth observation is looking bright – and super high-res!


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