Making Geospatial More Approachable Through Clear Documentation: Meet UP42’s Documentation Lead, Daria Lutchenko
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 geospatial industry can seem overwhelming, filled with complex terms and concepts that can often intimidate beginners. Clear and concise documentation is essential for making geospatial technology more approachable. It simplifies complex concepts, allowing users to quickly understand and utilize geospatial tools and data without feeling overwhelmed.
Daria Lutchenko works on this every day. As UP42’s Documentation Lead, her main goal is to explain things clearly, so that “even a beginner feels empowered to explore this beautiful world of geospatial data and the opportunities it presents.” Her words, not ours!
We spoke to Daria to find out more about democratizing access to geospatial data and analytics at UP42, and the role that documentation plays:
Q: Thanks for joining us, Daria! Could you tell us more about your journey to becoming Documentation Lead at UP42? What inspired you to go down the technical writing path?
A: No one ever knows what technical writing is when I tell them I’m a technical writer. For me, it was something I stumbled into by chance. But when I learned more about technical writing, I realized it was something I’d been doing in my head my whole life—like thinking of better ways to structure a board game rulebook, or wondering why they crammed ten sentences into one paragraph instead of splitting or simplifying it. I’ve never had much patience for wading through overly complicated text, so I feel at home making ideas more accessible.
Q: We loved reading your article on the art of documentation in the geospatial industry. How do you believe clear, concise, and consistent documentation makes a difference in the lives of those who use geospatial data? What are the main barriers beginners face when getting started with geospatial technology?
A: Similar to the point I just raised, people have far more important things to do than wander through a jungle of words. Getting to the point as fast as possible can make all the difference in whether people choose your product. The geospatial industry is especially guilty of this because of the sheer complexity of the source material. There’s only so much you can simplify when it comes to satellite specifications, for example. But I still believe it’s our responsibility—those of us who want to attract new people to the industry—to break down that complexity, so that people can use it in their respective work fields.
Q: Why do you think it’s important that geospatial documentation is approachable? What are the potential benefits and real-world impacts?
A: When documentation is easy to navigate and understand, people can focus on solving real-world problems instead of grappling with complex concepts. Clear documentation empowers people to turn to geospatial data for the insights they need in their industries. If we don’t explain how it works first, the adoption itself might never happen. So making it understood is the first step in this process.
Q: How does UP42’s approach to documentation align with its mission of democratizing access to geospatial data and tools?
A: I think of our documentation principles as a continuation of the “democratizing access” approach. You can’t achieve democratization without inclusivity, and that, in part, starts with the documentation—with the terms you use and your approach. Whether you gatekeep, even unintentionally, or proactively try to be approachable and comprehensive in your communication style, it makes a difference. Ultimately, it’s about ensuring that those who want to learn more about geospatial, can do so, regardless of how technical or experienced in the industry they are.
Q: UP42’s mission aligns perfectly with the industry’s need for democratizing access to geospatial data and tools, especially as the field becomes increasingly important in addressing global challenges such as climate change and disaster management. Can you share an example of how UP42’s documentation has simplified a particularly challenging geospatial concept?
A: It was a challenge to explain the differences in processing levels because providers use their own terminology and naming conventions. And at UP42, being positioned between providers and customers, we needed to find a way to create a unified leveling system. We spent some time going through provider manuals and research papers, and discussing our ideas with experienced colleagues. In the end, we came up with a leveling system and introduced matrix tables on our collections’ pages to provide users with an understanding of the processing levels associated with each data product.
Q: How do you address the varying needs of different user groups (such as developers, analysts, and decision-makers) in your documentation? For example, how do you balance providing technical details with maintaining clarity for non-expert users?
A: It’s easy to do with UP42 products because we offer different access options, tailored for different audiences. The API and SDK are designed for developers, while our console—the graphical interface—is intended for decision-makers. The console provides an overview of our products and offerings but is also suited for ordering itself, for those users who don’t need to build ordering pipelines with developer tools. So by following this approach in our documentation, the separation of audience flows comes naturally: we have API and SDK references and tutorials for developers, and console instructions for those using the graphical interface.
Q: We know how important it is to adhere to global English principles so that documentation is accessible and comprehensible to a diverse audience—including non-native English speakers or those unfamiliar with the industry. Are there any other best practices you follow in your documentation to ensure approachability?
A: We’re user advocates above all, so I think the first step would be to adopt this mindset, and everything else will follow. We have a few formalized principles. For example, we don’t just describe what our products can do, but instead explain how they can benefit the reader. I’ve already mentioned the principle of being mindful of people’s time—we achieve this by getting to the important thing fast, starting with the key takeaway. Another principle, a favorite of mine, is “function over form”. Basically, this means that when you’re presented with a choice between something that looks better and something that’s more accurate text-wise, you should always go with the more accurate option. Meaning is always more important than design.
Q: How does UP42 gather feedback on its documentation and incorporate user suggestions for improvement?
A: We regularly meet with the Support team to go over their tickets and find areas for improvement. The idea is to provide answers in the documentation upfront, to reduce support requests. We all suffer from the “curse of knowledge”—when you know the product, it’s hard to imagine what will be unclear to readers—so exercises like this help with getting out of your comfort zone.
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The Critical Role of Ground-Based Data in Regression Model Accuracy for Remote Sensing Applications
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.
Remote sensing has revolutionized environmental studies, allowing scientists to gather comprehensive data about the Earth’s surface and atmosphere from afar. From monitoring deforestation and mapping urban sprawl to assessing agricultural health and predicting natural disasters, the applications of remote sensing are vast and varied.
However, despite these technological advances, the accuracy of the regression models used in remote sensing heavily relies on ground-based data. This article delves into the indispensable role of ground-based data in calibrating and validating these models, thereby ensuring the reliability of remote sensing analytics.
Pixel-Level Detail: Categorizing Imagery with Labels and Values
Since the launch of Landsat-1 in 1972, satellite imagery has become increasingly accessible, marking over five decades of open-access satellite imagery covering the globe. With a spatial resolution of 30 meters, the Landsat constellation provides moderate-resolution imagery, enabling applications ranging from land cover classification to environmental monitoring. This resolution allows us to observe landscape features at a human scale while still covering large areas.
Today, multiple sources of open-access imagery, such as MODIS and the Sentinel constellation, as well as commercial options providing sub-meter spatial resolution, are available. These datasets are now easily accessible through platforms like UP42. Increased accessibility has propelled a wide range of applications, particularly in image classification. You’re now able to efficiently assign a class label to each pixel, such as:
- Water bodies
- Vegetation types
- Snow and ice cover
- Urban structures and buildings
- Agricultural fields
From Classification to Regression
Recently, there has been an increasing shift towards using satellite imagery for regression modeling. Instead of assigning a label, regression modeling assigns a quantitative value to each pixel. This is a shift from traditional semantic segmentation, which assigns class labels like ‘building or ‘nonbuilding’. Instead, regression models predict a continuous quantitative value for each pixel, such as crop yield or vegetation indices. This approach allows for:
- More granular information extraction
- Capturing subtle variations within a single land cover type
- Direct quantification of physical or biophysical parameters
Some key applications of regression modeling include estimating forest biomass, examining soil moisture, and looking at water quality parameters. For example, pixel-level regression is ideal in precision agriculture for predicting crop yield. It’s useful in urban planning for estimating building footprint height and can be used in environmental monitoring when quantifying vegetation indices.
A quick review of remote sensing studies indicates that a significant portion of studies focus on classification and change detection, whereas the applications using regression modeling are still being explored.
This trend toward pixel-level regression is driven by several factors:
- Demand for Granularity: There’s an increasing need for more detailed, quantitative information beyond simple categorical labels
- Richer Information: Regression models can provide more nuanced insights into continuous variables like vegetation health, soil moisture, or urban density.
- Improved Decision Support: Quantitative data often translates more directly into actionable insights. the need for more granular and quantitative information, beyond categorical labels.
While promising, the shift towards regression modeling in remote sensing comes with its own challenges. Regression models typically require more finely annotated training data, with continuous ground truth values for each pixel.
This is where ground-based data comes in.
The Critical Role of Ground-Based Data
Ground-based data, or “ground truth” data, is essential for validating and calibrating remote sensing observations. Collected directly from the Earth’s surface, it excludes atmospheric effects and other errors, ensuring accuracy in regression models. Key reasons for its importance include:
- Training Data: Proper calibration with field data minimizes errors. For instance, satellite sensors might misinterpret different surfaces. Ground data helps correct these inaccuracies.
- Model Validation: Validation with ground data ensures predictive accuracy. For example, crop yield models must be validated against actual field data to refine and improve reliability.
- Spatial Extrapolation: Ground data allows model predictions to be applied over larger areas. A model calibrated with soil moisture data from one region can estimate moisture levels in similar regions without direct measurements.
- Data Integration: Integrating ground data with high-resolution imagery from UAVs enhances the validation database, improving model accuracy. For example, combining groundwater quality measurements with UAV data enhances large-area predictions.
- Physical Interpretability: Ground data provides context, making models more transparent and their predictions more understandable.
- Error Correction and Bias Adjustment: Ground data helps identify and correct biases in satellite data, such as adjusting satellite-derived temperature estimates with ground-based readings for better accuracy.
Sources of Ground-Based Data for Remote Sensing Validation
Ground-based data plays a crucial role in calibrating and validating remote sensing models. Two primary sources of this data are field measurements and reflectance measurements.
- Field Measurements: These involve on-site data collection using instruments to measure various environmental parameters, such as soil moisture, vegetation type, and atmospheric conditions. For instance, scientists may use on-site turbidimeters to check the turbidity of the water. Additionally, samples collected from the field can be stored carefully and brought to the laboratory for analysis, such as chlorophyll concentration or the amount of nutrients in the water or soil sample.
- Reflectance Measurements: It is important to collect the reflectance spectra as satellite data is affected by atmospheric composition, these measurements help account for those atmospheric effects—providing a direct link between ground conditions and remotely sensed data.
Reflectance measurements are typically taken using a handheld spectrometer that provides hyperspectral reflectance values (typically from 390nm to 900nm with 1nm bandwidth).
Using Regression Models in Remote Sensing
Now that we have established the importance of ground-based or “in-situ” data for remote sensing applications, it is important to understand how to use regression models and their need. Put simply, regression models are statistical techniques used to predict continuous variables based on the spectral data obtained from satellite imagery.
Inputs for Regression Models
Regression models can utilize one or a combination of inputs:
- Original spectral bands such as Blue, Red, Green, etc.
- Band ratios and indices such as Blue/Green, or NDVI (Normalized Difference Vegetation Index)
- Spectrally transformed values such as PCA (Principal Component Analysis) and (Hue, Saturation, and Value)
- In-situ parameters, for instance, turbidity or suspended particulate matter, can be used in the estimation of water clarity or chlorophyll-a
- Environmental and meteorological parameters such as rainfall, air and water temperature, wind speed and direction
Thankfully, we do not have to manually calculate all these indices as platforms like UP42 offer processing tools for calculating various indices and processing data, streamlining the workflow for regression modeling.
How Do These Models Work?
Regression models establish relationships between dependent variables (e.g., soil moisture, and biomass) and independent variables (e.g., spectral bands from satellite imagery). The goal is to predict the value of the dependent variable for each pixel in an image based on the spectral information. Common types of regression models used include:
- Linear Regression: Establishes a linear relationship between the dependent and independent variables.
- Multiple Linear Regression: Involves more than one independent variable to predict the dependent variable.
- Non-linear Regression: Captures more complex relationships that are not linear, such as polynomial, logarithmic, or exponential.
- Machine Learning-Based Regression: Methods such as Random Forest, Support Vector Regression, and Neural Networks can model complex, non-linear relationships more effectively than traditional regression techniques.
Why Are These Models Needed?
Regression models are crucial for transforming spectral data into meaningful environmental parameters. Unlike classification models, which assign a predictive value to each of the satellite-based pixels and not a class label, regression models can be used to accurately map and monitor an environmental variable on a large spatial and temporal scale.
Challenges in Using Ground-Based Data
- Remote areas and difficult terrain hinder data collection, making it logistically and physically demanding.
- High costs for specialized equipment, travel, and personnel make extensive data collection expensive.
- Temporal discrepancies between ground data collection and satellite overpasses, along with natural environmental changes, lead to potential alignment issues.
Alternative Approaches
Given these challenges, researchers are exploring alternative methods to supplement ground-based data. High-resolution imagery, drone-based data, citizen science initiatives, sensor networks, and data fusion techniques, all mitigate some of the challenges associated with ground-based data collection. Platforms like UP42 offer access to a diverse array of geospatial data sources, enabling users to combine data types.
From drone data and high-resolution satellite imagery to aerial data and IoT sensor data streams—these platforms also offer tools for processing, analysis, and integration.
Real-World Applications and Impact
The integration of ground-based data in remote sensing applications significantly enhances the accuracy and reliability of the results. Here are a few real-world examples:
Vegetation Analysis
In agriculture, remote sensing is used to monitor crop health and predict yields. Ground-based measurements of crop parameters, such as leaf area index, biomass, and chlorophyll content, are essential for calibrating satellite-based vegetation indices.
This calibration ensures that remote sensing models accurately reflect the vegetation or forest conditions on the ground as demonstrated in the figure below. Additionally, tree canopy detection can be easily carried out using platforms like UP42.
Figure 1: Biomass density distribution in Beijing, China (a) Coniferous forest; (b) Mixed forest; (c) Broadleaf forest; (d) all forest sampling plots.
Water Quality Monitoring
Remote sensing is increasingly being used to assess and monitor water quality in lakes, rivers, and coastal areas.
Ground-based measurements of parameters like turbidity, Secchi Disk Depth (SDD) which is an indicator for water clarity, chlorophyll concentration, and nutrient levels are crucial for calibrating regression models. These models can then predict water quality parameters over large areas, providing essential data for environmental management and policy-making.
For example, satellite-derived estimates of chlorophyll concentration and water clarity in a lake can be validated with in-situ measurements to ensure accurate monitoring of algal blooms, which can be harmful to aquatic life and human health. One such example over Canandaigua Lake is presented in this study and the SDD maps are shown below.
Figure 2: Temporal maps showing the estimated SDD for Canandaigua Lake using one clear imagery for each of the months from June through October for years 2020 (a)–(d), 2021 (e)–(i) and 2022 (j)–(n). The red color indicates the lower values of SDD indicating turbid water and the blue value indicates high values of SDD indicating clearer waters.
Soil Moisture Monitoring
Soil moisture is a critical parameter for agricultural management, drought prediction, and hydrological studies. Remote sensing provides large-scale soil moisture data, but these estimates need to be validated with ground-based measurements as illustrated from a study below.
Ground-based sensors and manual sampling provide accurate soil moisture data that are used to calibrate and validate satellite-derived models. This ensures that the remote sensing models accurately reflect the soil moisture conditions, which is essential for effective water resource management, irrigation planning, and understanding the impacts of climate change on agriculture.
Figure 3: Predictive maps of soil moisture based on the generalized model for both the Cubist and Random Forest MLAs in the study area for the four months.
Conclusion: The Synergy of Ground-Based Data and Remote Sensing
Ground-based data is a cornerstone of ensuring the accuracy and reliability of regression models in remote sensing applications. While remote sensing technology provides a broad overview of environmental conditions, ground-based data offers the detailed, localized information necessary for precise model calibration and validation.
By combining the strengths of remote sensing and ground-based observations, we can achieve more accurate and reliable environmental insights, ultimately leading to better decision-making and resource management. The future of Earth observation and analysis lies in this integrated approach, combining the global perspective of satellites with the detailed, nuanced information provided by ground-based observations.
Emerging technologies like drones, IoT sensors, and AI/ML offer solutions to overcome challenges in ground-based data collection by providing high-resolution data, real-time monitoring, and efficient data processing, reducing costs and enhancing compatibility with satellite data. Therefore, despite the challenges of collecting ground-based data, the integration of new technologies promises to enhance the efficiency and accuracy of this crucial process.
