From Pixels to Prediction: A 5-Stage Framework for Geoscience Event Modeling¶
The modern challenge in Earth science is no longer the acquisition of data, but the extraction of knowledge. Our satellites provide a continuous, dense stream of physical measurements—a torrent of pixels representing temperature, elevation, and reflectance. However, true understanding comes from identifying the discrete, meaningful events embedded within this data: the landslide, the wildfire, the flood. This report outlines a five-stage framework for moving beyond simple monitoring to create a robust, learning-based system that can detect, analyze, and ultimately forecast these critical geoscience events.
To make this framework concrete, we will follow four running examples through each stage:
Wildfire Ignition: The initial spark and spread of a new wildfire.
Methane Super-Emitter: A massive, transient greenhouse gas leak from an industrial facility.
Riverine & Coastal Flooding: The inundation of normally dry land by overflowing rivers or storm surge.
Harmful Algal Blooms (HABs): The rapid, uncontrolled growth of toxic algae in coastal and freshwater systems.
Stage 1: Acquisition, Labeling, and Supervised Learning¶
ELI5: This stage is like teaching a toddler to recognize a cat. You don’t just tell them about cats; you show them hundreds of pictures, pointing each time and saying, “That’s a cat.” You show them big cats, small cats, black cats, and striped cats. Over time, their brain learns the general “pattern” of a cat.
Technical Introduction: The primary objective of this stage is to generate a high-fidelity, labeled dataset to serve as “ground truth” for a supervised learning algorithm. The process involves human-in-the-loop annotation, where domain experts perform feature extraction on raw observational data (e.g., multispectral imagery, radar interferograms) to identify and delineate event signatures. This creates a corpus of training examples, where the input is the raw sensor data and the output is a semantic mask or vector representing the event’s class and geometry.
Process: The cycle begins with the systematic acquisition of raw satellite data (e.g., daily imagery from Sentinel-2, interferograms from Sentinel-1). In parallel, a team of domain experts acts as “analysts-in-the-loop.” They meticulously examine this data, using their knowledge to manually identify and label specific events. For instance, an analyst might delineate the boundary of a new forest clearing, mark the initiation point of a landslide, or classify a thermal anomaly as a new wildfire ignition.
Outcome: The result is a high-quality, labeled training dataset—a “ground truth” catalog where raw observations are explicitly linked to event outcomes. This dataset is then used to train a supervised machine learning model (e.g., a Convolutional Neural Network or a U-Net for image segmentation) to replicate the analyst’s decision-making process.
Key Challenge: This stage is labor-intensive and requires deep domain expertise. The goal is not just to label data, but to create a robust and diverse training set that captures the full variability of an event, ensuring the subsequent model is accurate and does not develop significant biases.
Examples in Practice¶
Wildfire Ignition: Analysts review daily thermal data from sensors like GOES and VIIRS. When they spot a new, persistent hotspot that grows over time, they label it by drawing a point feature at its ignition location and noting the exact time of first detection.
Methane Super-Emitter: An atmospheric scientist analyzes TROPOMI data to find a regional methane anomaly, then examines high-resolution GHGSat imagery for that location. They label the event by placing a point feature on the source facility and drawing a polygon around the visible plume.
Riverine & Coastal Flooding: Hydrologists compare before-and-after Sentinel-1 radar images following a major storm. They manually draw a polygon feature that outlines the full extent of the inundated area, which appears dark in the radar imagery.
Harmful Algal Blooms (HABs): Oceanographers analyze daily Sentinel-3 ocean color data. When they detect high concentrations of chlorophyll-a, they label the event by drawing a polygon that encompasses the bloom.
Model Families & Techniques¶
This stage is dominated by supervised learning models. For events represented as polygons (like floods or burn scars), semantic segmentation models (e.g., U-Nets, DeepLab) are common. For events represented as points or bounding boxes (like wildfire ignitions or industrial facilities), object detection models (e.g., YOLO, R-CNN families) are used. For simple “event vs. no-event” pixel classification, more traditional machine learning models like Random Forests or Gradient Boosted Trees can also be highly effective.
Stage 2: Automated Discovery and Near-Real-Time Monitoring¶
ELI5: Now that the toddler knows what a cat looks like, you can give them a new book, and they can go through it themselves, pointing out all the cats without your help. This is much faster than you doing it, and it means you can read many more books together.
Technical Introduction: The objective of this stage is to operationalize the trained model in a high-throughput, low-latency inference pipeline. This involves deploying the model into a scalable computing environment (typically cloud-based) and integrating it with real-time data streams from satellite ground stations. The system is designed for automated event detection, transforming the manual, reactive process into a continuous, proactive monitoring capability.
Process: The validated model from Stage 1 is deployed into a cloud-based data pipeline. Instead of analysts searching for events, the model automatically scans every single incoming satellite observation in near-real-time. It sifts through terabytes of data, flagging “candidate events” that match the patterns it learned to recognize. A human analyst may still be kept “on-the-loop” for rapid quality control, quickly validating the model’s most critical or uncertain findings.
Outcome: This stage dramatically increases the speed and scale of monitoring. A task that would take a team of analysts weeks can be completed by the model in minutes. The output is a live, streaming feed of newly detected events, enabling rapid response and situational awareness.
Key Challenge: This requires significant computational infrastructure to process data at scale. The model must also be robust enough to handle the diversity of real-world data, including clouds, sensor noise, and novel event variations not present in the initial training set.
Examples in Practice¶
Wildfire Ignition: The trained model automatically scans all incoming GOES thermal data. Within five minutes of a new ignition, the system flags the hotspot, determines its location, and sends an automated alert to fire agencies.
Methane Super-Emitter: The model continuously scans all global TROPOMI data. When it detects a significant methane spike, it automatically cross-references the location with a facility database and flags it for a high-resolution follow-up scan by another satellite.
Riverine & Coastal Flooding: The U-Net model automatically ingests every new Sentinel-1 acquisition and compares it to a baseline water mask. When it detects a significant new area of water, it generates a flood extent map and sends an alert to emergency managers.
Harmful Algal Blooms (HABs): The model scans daily ocean color imagery from Sentinel-3. When it identifies a new, growing bloom, it automatically issues an alert to public health departments and fisheries to enable shellfish bed closures.
Model Families & Techniques¶
The models used here are the deployed versions of those trained in Stage 1. The focus shifts to efficient inference. Additionally, unsupervised anomaly detection models (e.g., Isolation Forests, Autoencoders) can be used in parallel to flag novel or unusual patterns that don’t fit the training data, helping to identify new event types or model failures.
Stage 3: Historical Reanalysis and Catalog Creation¶
ELI5: You find a giant box of old family photo albums from before the toddler was born. You give them the whole box and say, “Find every single picture of a cat in here.” The next day, you have a complete scrapbook of every cat your family has ever owned, all neatly organized.
Technical Introduction: The objective here is retrospective data processing, or “back-processing,” to create a consistent, long-term event catalog. This involves applying the validated inference model to the entirety of a mission’s historical data archive. The process requires data homogenization to account for changes in sensor calibration and processing versions over time, ensuring a consistent baseline. The output is a structured spatio-temporal database, transforming the unstructured archive of pixels into a queryable knowledge base.
Process: The automated model is applied retrospectively to the entire historical satellite archive (e.g., the decades-long Landsat, ASTER, or AVHRR archives). This massive-scale computational task “discovers” thousands of events that occurred before the monitoring program began, finding historical precedents and establishing a deep baseline of activity.
Outcome: The output is not a series of images, but a structured, spatio-temporal event catalog. This is a database where each event is an entry with attributes like start/end time, location (point or polygon), magnitude, duration, and type. This transforms unstructured pixel data into a structured dataset ready for high-level analysis.
Key Challenge: Processing petabytes of historical data is computationally expensive. Furthermore, sensor characteristics change over time, requiring the model to be robust or adapted to handle data from different historical missions to ensure the resulting catalog is consistent and free of instrument-driven biases.
Examples in Practice¶
Wildfire Ignition: The model is run on 40 years of Landsat thermal data, creating a comprehensive database of historical fire ignitions. Each entry includes the date, location, and the estimated initial size of the fire.
Methane Super-Emitter: The model is run on the full TROPOMI archive, identifying hundreds of major emission events that were missed in initial manual surveys. This creates a historical catalog of industrial super-emitter events, each with a location, date, and estimated emission magnitude.
Riverine & Coastal Flooding: The model processes the complete radar archives (Sentinel-1, ERS, Envisat), creating a global catalog of major flood events, with each entry containing the flood’s maximum extent, duration, and the river basin affected.
Harmful Algal Blooms (HABs): The model is run on the multi-decadal ocean color archives (SeaWiFS, MODIS, VIIRS), producing a global historical catalog of HAB events, detailing their timing, location, magnitude, and duration.
Model Families & Techniques¶
While the discovery model from Stage 2 is the primary tool, this stage can be enhanced with data assimilation techniques. For example, if a physical model of a process exists (like a smoke dispersion model), techniques like a Kalman filter or variational assimilation (4D-Var) can be used to optimally blend the sparse, detected events from the satellite data with the continuous, physically-consistent output of the model. This creates a complete “reanalysis” field that fills in the gaps between observations.
Stage 4: Trend and Relational Analysis¶
ELI5: You and the toddler look through the finished “cat scrapbook.” You start noticing interesting patterns, like “Hey, there seem to be more pictures of cats every year,” and “Look, almost every time there’s a picture of Grandma’s couch, there’s a cat sleeping on it.”
Technical Introduction: The objective of this stage is knowledge discovery through data mining and statistical analysis of the historical event catalog. This involves applying techniques like time-series analysis to detect secular trends, hotspot analysis (e.g., Getis-Ord Gi*) to identify statistically significant spatial clusters, and causal inference methods to investigate relationships between different event types. The goal is to extract scientifically meaningful patterns and drivers from the sparse event data.
Process: Data scientists and domain experts query the event catalog to investigate scientific hypotheses. They can perform time-series analysis to identify trends (Are wildfires becoming more intense?), hotspot analysis to find geographic clusters (Where are landslides most common?), and relational queries to find connections (Do flash droughts increase the probability of a severe fire season in the following year?).
Outcome: This stage generates scientific insight and quantifiable understanding. It allows us to understand the underlying drivers of events, identify cascading hazards (where one event triggers another), and assess how event frequency and magnitude are changing over time in response to climate change or human activity.
Key Challenge: The primary challenge is asking the right questions. The analysis requires a sophisticated blend of statistical methods, data science expertise, and deep knowledge of the Earth system processes being investigated. Correlation does not imply causation, and rigorous scientific validation is crucial.
Examples in Practice¶
Wildfire Ignition: Analysis of the historical catalog reveals that fire season start dates are trending earlier by 1.5 days per decade. It also shows a strong correlation between ignition locations and areas that experienced a severe drought in the preceding year.
Methane Super-Emitter: Analysis of the catalog reveals that 80% of super-emitter events in a given oil basin come from just 5% of the facilities. It also identifies a trend where events are more frequent during specific operational periods, like well completions.
Riverine & Coastal Flooding: The flood catalog shows a statistically significant increase in the frequency of major floods in coastal watersheds. A relational query reveals that 70% of the most damaging floods are associated with atmospheric river events.
Harmful Algal Blooms (HABs): Analysis of the historical catalog shows a clear trend of increasing HAB duration in the summer months. It also identifies a strong correlation between the onset of a bloom and sea surface temperatures exceeding a specific regional threshold.
Model Families & Techniques¶
This stage leverages a wide range of statistical and spatio-temporal analysis models. To understand how relationships vary over space, Geographically Weighted Regression (GWR) is a powerful tool. To find clusters, techniques like DBSCAN or hotspot analysis are used. For trend analysis, time-series decomposition models (e.g., Seasonal and Trend decomposition using Loess) are applied. To investigate connections between event types, causal inference frameworks can help move beyond simple correlation.
Stage 5: Forecasting and Predictive Modeling¶
ELI5: The next time your family gets in the car, the toddler says, “We’re going to Grandma’s house. She has a couch. So, I think there’s a very high chance we will see a cat today!” They’ve used their past knowledge of patterns to make a prediction about the future.
Technical Introduction: The final objective is to develop a predictive capability by building forecasting models based on the empirical relationships discovered in the previous stage. This involves integrating the historical event catalog with external predictive variables (covariates), often from numerical weather prediction (NWP) models or climate projections. The result is a probabilistic forecasting system that generates dynamic risk maps, quantifying the likelihood of a future event as a function of evolving environmental conditions.
Process: The trends and relationships discovered in Stage 4 are used to build a new class of predictive models. These models ingest real-time data streams of precursory variables (e.g., current soil moisture, rainfall forecasts, ground deformation rates) and calculate the probability of a future event occurring. The output is not a simple “yes” or “no,” but a dynamic risk map.
Outcome: This provides actionable intelligence for hazard mitigation and risk management. A forecast might say, “Given the forecasted rainfall from the GFS model and the current ground saturation measured by SMAP, this region has a 75% probability of experiencing landslides in the next 48 hours.”
Key Challenge: Forecasting is inherently probabilistic. The models must be carefully validated, and their uncertainty must be clearly communicated to end-users. This stage requires the successful integration of satellite observations with other data sources, particularly weather and climate model outputs, to create a truly predictive capability. This entire framework is a virtuous cycle, where the successes and failures of forecasting can be used to refine the initial event detection models, creating an ever-improving system of planetary understanding.
Examples in Practice¶
Wildfire Ignition: A model is built that ingests weather forecasts and real-time vegetation dryness data from MODIS. It produces a daily map of wildfire ignition probability for the next 72 hours, helping agencies pre-position resources.
Methane Super-Emitter: A predictive model uses the historical event catalog along with facility type, age, and weather patterns (which can affect operations) to create a risk map. This highlights which facilities are most likely to have a major emission event, guiding priorities for aerial or on-the-ground inspection campaigns.
Riverine & Coastal Flooding: A forecasting model ingests real-time rainfall data from GPM and rainfall forecasts from weather models. It combines this with a hydrological model to produce a 3-day forecast of river discharge and potential flood inundation areas.
Harmful Algal Blooms (HABs): A predictive model ingests sea surface temperature forecasts and ocean current models. It produces a weekly risk map highlighting coastal areas with a high probability of HAB formation, allowing for proactive water quality testing.
Model Families & Techniques¶
This stage is the domain of prognostic and forecasting models. The core task is often framed as a classification or regression problem where the goal is to predict the probability of an event. This can involve logistic regression, tree-based models, or more complex deep learning approaches that can handle time-series data, such as Long Short-Term Memory (LSTM) networks or Transformers. A key part of this stage is the inclusion of external covariates (e.g., weather forecasts, climate indices, static maps of terrain or infrastructure) that were identified as important drivers in Stage 4.