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Every day, plenty happens in a world as big as ours, and tracking the positive and negative changes globally is difficult given the physical scale of the planet. From understanding the impact of floods and wildfires to monitoring forest regrowth and endangered animal populations, having a bird’s-eye view through satellite data certainly helps. That’s just part of the puzzle, though. The data produced at that level are immense in size and volume, so it is crucial to leverage computer vision algorithms that help make sense of the data in a timely fashion.
By combining large-scale imagery with advanced machine learning models, experts in many areas, such as environmental and climate sciences, can dramatically accelerate their workflows and also cover more ground than previously possible, automating processes that were once entirely manual. Although the use cases seem disparate, at a high level, the pipeline is the same. Following the pipeline of data collection, preprocessing, feature extraction, modeling, and postprocessing, all these applications can unlock the powerful combination of computer vision and satellite data.
Broadly speaking, the phrase “satellite data” sounds like it refers to a homogenous format, but several types of satellite data exist, each varying in terms of use case. The four high-level categories—all with distinct subtypes—are optical, radar, thermal infrared, and light detection and ranging (lidar). Applications like vegetation analysis and precision farming are possible using optical satellite imagery, which includes both photographic and hyperspectral images to capture light the human eye can’t see. For tasks like topography mapping, disaster response, and landslide tracking, radar is a better option. With its modalities of synthetic aperture radar (SAR) and interferometric SAR, radar can penetrate clouds and darkness as well as build maps of surface deformations over time. Thermal infrared measures heat from surfaces and temperature fluctuations, making it great for measuring urban heat and evaluating energy efficiency. Finally, lidar creates three-dimensional point clouds, making it useful for managing forests and urban planning. Then, for a slightly closer view, aerial surveys are another high-resolution imagery format that helps unlock certain environmental applications, such as monitoring animal populations. All these nuanced types of imagery accompany the overall challenges of working with satellite data as well as the complexities unique to the specific modalities.
The biggest issues in working with any type of satellite imagery are the sheer size of each image and the fact that thousands or more images may need to be processed. Often, one image is hundreds of megabytes to several gigabytes, especially for hyperspectral or SAR data. As a result, one dataset could contain petabytes of data. Though some cloud platforms, such as Google Earth Engine, allow streaming analysis, local processing is more common and typically requires tiling or splitting the large image into smaller fragments or chips that can be loaded into memory. Having to split all the images means that the number of images to analyze dramatically increases, with the exact multiplier depending on resolution, sensor type, and processing method. Finally, to perform specific analyses of change or interpretations from the imagery relevant to an application, the chosen areas of interest must be manually segmented. These areas might include sections showing new growth, flooding, or ice coverage. Without the automation capabilities of computer vision, this segmentation is extremely time-consuming and labor-intensive.
The many applications of computer vision for satellite data, whether for flood modeling, agricultural analysis, or environmental monitoring, typically follow a shared high-level pipeline: data collection, preprocessing, feature extraction, modeling, and postprocessing for interpretation.
The pipeline begins with data collection. Satellite imagery is sourced from services like Google Earth Engine, NASA’s Landsat archives, or Copernicus data, which includes the European Space Agency’s (ESA’s) Sentinel-1 and Sentinel-2. These sources offer trade-offs in spatial resolution (meters per pixel), temporal resolution (time between captures), and available spectral bands. For example, researchers estimating rice crop productivity in Bangladesh selected imagery that provided the clearest recurring views of specific agricultural plots, relying on multiple revisit cycles to capture key growing periods.[1]
After the data are collected, they must be preprocessed, which involves both automated and manual cleanup to prepare images for modeling. Public datasets like Sentinel-2 already apply atmospheric corrections and allow filtering by cloud coverage, but additional adjustments—such as radiometric calibration, terrain correction, or speckle noise filtering (especially for SAR images)—may be needed. When analyzing regions across multiple time points, consistent temporal spacing is often introduced via interpolation. Crucially, due to the size of satellite data, images are then tiled into smaller sections so that models can ingest them.
Once imagery is prepared, computer vision models extract meaningful information from raw pixels. In the case of the Bangladesh study, researchers used convolutional neural networks (CNNs) to detect spectral patterns associated with crop health and phenology, learning these patterns from labeled samples tied to known field data. This automated feature extraction replaces older, manual methods like visual interpretation or simpler indices such as Normalized Difference Vegetation Index (NDVI) and allows the model to generalize across unseen plots and changing conditions. Researchers estimating rice crops in Bangladesh used this automated process to augment the conventional method of field survey collection, which is labor-intensive and often limited in scope. In cases where data are collected over time, temporal modeling is layered on top of the extracted features. Time series models, such as autoregressive integrated moving average (ARIMA) models, long short-term memory (LSTM) networks, or temporal CNNs, can identify trends, cycles, or changes, enhancing the analysis. For crop prediction, this helps distinguish between short-term variations and meaningful seasonal patterns tied to yield.
Finally, postprocessing translates model outputs, such as crop classifications or growth stage predictions, into actionable insights. In the rice agriculture example, estimates of rice yield and productivity were aggregated by region, creating a scalable alternative to manual field surveys. These outputs supported decisions around food security and resource allocation, particularly in regions where traditional data collection is expensive or impractical. By combining satellite data with computer vision in a structured pipeline, researchers and practitioners can transform vast, complex imagery into targeted, high-impact interventions, surpassing the limitations of legacy fieldwork and incomplete physical models.
Satellite data is changing the game when it comes to monitoring and measuring the world’s phenomena in critical industries, and computer vision is there to make that data usable. Scientists and engineers across many domains are increasingly leveraging automated computer vision tools to accelerate their analysis of satellite imagery, augmenting or replacing traditional workflows. Most applications share a general pipeline where raw imagery is collected and preprocessed, then passed through computer vision models, which extract relevant features like flooded areas or vegetation of interest. These outputs are then postprocessed and interpreted for downstream application-specific tasks such as insurance modeling or conservation planning. This approach dramatically improves the speed, scale, and consistency of satellite data analysis, transforming what once took teams of experts weeks to accomplish into near real-time, repeatable processes.
[1] https://cnr.ncsu.edu/news/2024/12/machine-learning-and-satellite-imagery-could-help-protect-the-worlds-most-important-crops/.
Becks Simpson is a Machine Learning Lead at AlleyCorp Nord where developers, product designers and ML specialists work alongside clients to bring their AI product dreams to life. She has worked across the spectrum in deep learning and machine learning from investigating novel deep learning methods and applying research directly for solving real world problems to architecting pipelines and platforms to train and deploy AI models in the wild and advising startups on their AI and data strategies.