Hyperspectral Imaging (HSI) supports the efficient management of urban resources and infrastructure. Traditional methods of monitoring and analysing urban environments often fall short in providing detailed, accurate, and timely data. HSI overcomes these limitations by offering a comprehensive view of urban landscapes, which can significantly enhance decision-making processes in areas such as environmental monitoring, infrastructure maintenance, and public safety.
Hyperspectral imaging is an advanced remote sensing technology that captures and processes information across a wide spectrum of light beyond what the human eye can perceive. Unlike standard imaging systems that capture images in three colour bands (Red, Green, and Blue), HSI systems divide the spectrum into hundreds of narrow spectral bands. This detailed spectral data enables the identification of materials, detection of chemical compositions, and assessment of various physical properties with unparalleled precision. For instance, HSI can detect pollutants in the air, identify different materials used in buildings, and monitor vegetation health in urban parks.
Implementing HSI in urban settings involves mounting hyperspectral sensors on satellites, drones, or ground-based platforms. These sensors collect data by scanning the urban area and recording the spectral signature of each pixel in the image. Advanced algorithms then process this data to create detailed maps and reports that can be used by city planners, environmental agencies, and infrastructure managers. This technology not only improves the accuracy of urban analysis but also speeds up the data collection process, allowing for real-time monitoring and rapid response to emerging issues.
As urban areas continue to expand and face increasing pressures from climate change, pollution, and population growth, the need for precise and efficient management tools becomes essential. HSI provides a level of detail and accuracy that can lead to more sustainable urban development. For example, it can help cities reduce their carbon footprint by identifying areas where energy efficiency can be improved , or renewable energy sources can be optimally deployed.
In disaster response, HSI provides rapid and accurate damage assessments, identifies structurally compromised buildings, and locates trapped individuals, improving emergency response efficiency. For infrastructure maintenance, HSI detects early signs of wear and tear in buildings, bridges, and roads, allowing for timely interventions that prevent costly repairs and enhance safety. Additionally, HSI supports urban planning by providing detailed data for designing sustainable and resilient cities, integrating green spaces, and optimising land use.
Designs and manufactures high-performance hyperspectral imaging systems for remote sensing and industrial machine vision.
Conducts advanced research into cryogenic CMOS and quantum computing interconnects.
Developed and operates the EMIT (Earth Surface Mineral Dust Source Investigation) hyperspectral instrument on the ISS.
A global leader in hyperspectral imaging instrumentation and systems, now part of Konica Minolta.
Building a constellation of hyperspectral earth imaging satellites to provide daily global coverage.
Provides space-based hyperspectral monitoring solutions, focusing on energy infrastructure and environmental compliance.
A leading provider of global daily earth data, developing the Tanager hyperspectral satellite constellation.
Manufactures hyperspectral imaging cameras and systems for laboratory, outdoor, and airborne applications.
A vertically integrated geospatial company that includes hyperspectral capabilities in its satellite fleet.
Developing high-resolution hyperspectral imaging satellites using deployable optics.
Paper
Advancing Urban Development: Applications of Hyperspectral Imaging in Smart City Innovations and Sustainable SolutionsMDPI Smart Cities · Mar 14, 2025
This study explores the integration of hyperspectral imaging with AI and IoT to enhance smart city applications, specifically for urban planning, environmental monitoring, and sustainable development.
Paper
Hyperspectral imaging | Nature Reviews Methods PrimersNature Reviews Methods Primers · Feb 26, 2026
This primer details the workflow and data processing pipeline of hyperspectral imaging (HSI), emphasizing its role as a non-invasive sensing modality for characterizing material, chemical, and biological properties in Earth observation and other fields.
Paper
Airborne and Spaceborne Hyperspectral Remote Sensing in Urban Areas: Methods, Applications, and TrendsMDPI Remote Sensing · Sep 8, 2025
A comprehensive review of airborne and spaceborne hyperspectral remote sensing methods specifically applied to urban areas, discussing trends in data acquisition and processing for city management.
Article
Smart cities and hyperspectral imaging: The future of urban sustainabilityDevDiscourse · Mar 15, 2025
Discusses how hyperspectral imaging provides high-resolution spectral data for urban planners to assess land use, monitor infrastructure, and detect environmental changes.
Article
GeoCarbyoutube.com
GeoCarb is a NASA Mission that will focus on mapping concentrations of key carbon gases from a new vantage point: geostationary orbit.. Observations from the mission will provide climate scientists with the ability to look at the climate cycle on seasonal time scales, drastically increasing our ability to understand how the earth is responding to climate change.
Article
GeoCarb - The Geostationary Carbon Cycle Observatoryou.edu
The Geostationary Carbon Cycle Observatory (GeoCarb), a first-of-its-kind space-based earth science mission, will study how and why the global carbon cycle is changing, and monitor plant health and vegetation stress throughout the Americas.
Article
Hyperspectral Imaging from Spacegisgeography.com
Imagine seeing the world in more spectral detail so you can better understand and discern anything on our planet. Both multispectral and hyperspectral imaging captures reflected light.
Article
NASA Instrument Detects Dozens of Methane Super-emitters from Spacereuters.com
An orbital NASA instrument designed mainly to advance studies of airborne dust and its effects on climate change has proven adept at another key Earth-science function - detecting large, worldwide emissions of methane, a potent greenhouse gas.
Article
Hyperspectral Imaging Algorithms and Applications: A Reviewtechrxiv.org
Hyperspectral Imaging (HSI) provides detailed spectral information for each pixel in an image, which involves acquiring images at numerous narrow and contiguous wavelength bands. Comprehensive spatial and spectral information deposits in hyperspectral images acquired by sensors, cameras, and various data acquisition sources lead to a wide range of applications across multiple fields from agriculture, and environment to biology. Various Image Processing and Artificial Intelligence algorithms have been developed periodically to analyze the data acquired through HSI. This review paper presents a comprehensive analysis of HSI focusing on its various aspects and potential implications. We explore detailed applications and key algorithms of HSI and discuss the associated advancements and challenges. Through an extensive literature review, we identify the state of research and methodologies related to HSI. Our study covers a wide range of HSI applications such as Earth Sciences, Exploration, Monitoring, Agriculture, Security, Conservation, Security, Healthcare, and Medical Imaging, and how Hyperspectral Imaging algorithms benefit these applications. Additionally, we discuss emerging trends and future directions in HSI providing insights into the promising avenues for further research.
Article
Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Preventionmdpi.com
Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. The rapid development of remote sensing has made it possible to study environmental processes and changes in agriculture and also to provide important assistance in relevant practices. The goal of this Special Issue is to collect the latest developments in the application fields of precision agriculture and fire preventions. Both these two contexts were traditionally on-field tests for computer vision-based algorithms and methodologies. With the growing availability of hyperspectral sensors—that are more effective compared to multispectral remote ones—the approach to fire prevention and precision agriculture is quite different, providing an unexpected and powerful support to workers. Papers on the latest research challenges, case studies and on-field applications, limitations, and advantages of different platforms and sensors as well as future perspectives are welcomed.
Article
Satellite Observations to Support Monitoring of Greenhouse Gas Emissionsimperial.ac.uk
Satellites produce high-resolution global observations of Earth’s surface and atmosphere that provide information about greenhouse gas emissions.
Article
How Satellite Imagery is Helping to Transform Soil Carbon Monitoring For Growersdeepplanet.ai
Healthy soil systems act as an important natural carbon sink for the planet by sequestering atmospheric carbon dioxide and helping to reduce greenhouse gas concentrations. The value of our planet’s soil as a carbon store cannot be overlook
Article
Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overviewmdpi.com
There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.
