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Tutorials Throughout The Semester
Week 6 - Getting Started With Remote Sensing
The Esri course Getting Started with Imagery and Remote Sensing is an introductory tutorial designed to help GIS users understand how remotely sensed imagery can be used as more than just a visual backdrop. The tutorial walks learners through the fundamentals of remote sensing, including how sensors on satellites, drones, and aircraft collect data about the Earth’s surface. It also introduces how this imagery is stored as raster data and how it can be visualized, processed, and analyzed within GIS platforms like ArcGIS.
This tutorial was especially useful because it shifted the perspective of imagery from being simply a basemap to being a powerful data source. Instead of just displaying images, the course demonstrates how to extract meaningful information from them, such as identifying land cover, detecting changes over time, and analyzing environmental patterns. It also explains how the raster information model works, allowing users to better interpret pixel-based data and apply it to real-world scenarios.
One of the main benefits of this tutorial is that it builds a strong foundation for using imagery in GIS analysis. By learning how to integrate remotely sensed data with other geospatial layers, users can create more detailed and accurate representations of the real world. This leads to better decision-making in fields like environmental monitoring, urban planning, agriculture, and disaster response. Additionally, the tutorial emphasizes practical, hands-on skills, helping users gain confidence in working with imagery tools and workflows that are widely used in the geospatial industry.
Overall, this tutorial is valuable because it transforms how users think about imagery in GIS. Rather than treating it as a background element, it highlights imagery as a critical source of data that can be analyzed, interpreted, and used to solve real-world problems.
Week 7 - Choose Your Own Adventure
Deep Learning:
The ArcGIS Learn project Detect Objects with Text SAM was very useful, easy to follow, and helped significantly in understanding how deep learning can be applied in GIS. The tutorial walks step-by-step through using the Text SAM GeoAI model in ArcGIS Pro to automatically detect objects, such as boats, from high-resolution imagery. It clearly explains how to set up the project, run the deep learning tool, and refine the results, making it approachable even for beginners working with imagery analysis.
One of the most helpful aspects of this tutorial is how it simplifies complex concepts like object detection and GeoAI. Instead of manually identifying features in imagery, the workflow uses text prompts to automatically extract objects and convert them into usable GIS data layers. This not only saves time but also demonstrates how powerful and efficient modern geospatial tools can be when combined with deep learning models.
Overall, this project was beneficial because it provided hands-on experience with real-world applications of remote sensing and artificial intelligence. It helped build confidence in using advanced tools within ArcGIS and showed how imagery can be analyzed to support decision-making, such as urban planning or environmental monitoring. The clear instructions and practical workflow made it an effective learning experience that reinforced key GIS and remote sensing concepts.
Performing Viewshed Analysis with ArcGIS Pro
The Esri course Performing Viewshed Analysis in ArcGIS Pro helped significantly in understanding visibility analysis within GIS. The tutorial guides users through the process of determining what areas are visible from a specific location using elevation data and observer points. It clearly explains how to set up a viewshed analysis, adjust parameters such as viewing distance and height, and interpret the results within a 3D environment. A viewshed essentially identifies which parts of a landscape can be seen from a given viewpoint, making it a powerful tool for spatial analysis.
One of the most helpful aspects of this tutorial is how it connects the technical workflow to real-world applications. By adjusting observer position, direction, and field of view, users can model scenarios such as placing security cameras, evaluating line-of-sight for infrastructure, or analyzing terrain visibility. The step-by-step instructions make it easy to understand even complex concepts, allowing users to confidently perform their own analyses in ArcGIS Pro.
Overall, this tutorial was beneficial because it demonstrated how elevation and raster data can be used to solve practical problems involving visibility. It helped reinforce key GIS skills while introducing a valuable analytical tool that can be applied in areas such as urban planning, environmental management, and security planning. The simplicity of the workflow, combined with its real-world usefulness, made it an effective and impactful learning experience.
Performing Line of Sight Analysis
The Esri course Performing Line of Sight Analysis helped a lot in understanding visibility analysis in a more detailed and practical way. The tutorial walks through how to determine whether a specific target is visible from an observer’s location using elevation data and 3D scenes in ArcGIS Pro. It clearly explains how to place observer and target points, adjust heights and distances, and interpret the results, making the workflow straightforward even for beginners.
One of the most helpful aspects of this tutorial is how it demonstrates the concept of line of sight as a direct visibility check between two points. Instead of analyzing an entire area like viewshed analysis, this method focuses on whether a specific object can be seen from a particular location. The results are visually represented, often showing visible portions in green and obstructed portions in red, which makes it easy to quickly understand terrain impacts and obstructions such as buildings or trees.
Overall, this tutorial was beneficial because it showed how line of sight analysis can be applied to real-world scenarios such as placing surveillance systems, planning communication towers, or evaluating terrain for security and infrastructure purposes. It reinforced key GIS concepts while introducing a practical tool that is both efficient and highly relevant for decision-making. The clear instructions and hands-on approach made it an effective and valuable learning experience.
Classify Land Cover to Measure Shrinking Lakes
The ArcGIS Learn project, Classify Land Cover to Measure Shrinking Lakes helped a lot in understanding how remote sensing can be used to analyze environmental change over time. The tutorial walks step-by-step through comparing satellite imagery from different years to observe how a lake has changed, specifically focusing on Lake Poyang in China. It guides users through setting up the project, visualizing imagery from multiple dates, and preparing the data for analysis in ArcGIS Pro.
One of the most helpful aspects of this project is learning how to perform land cover classification using raster imagery. By applying an unsupervised classification method, the tutorial shows how pixels with similar spectral values can be grouped into categories such as water, vegetation, and other land types. This allows users to clearly identify the lake’s boundaries and distinguish it from surrounding land cover. The process of cleaning and refining the classification also helps improve accuracy and better represent real-world conditions.
Overall, this tutorial was beneficial because it demonstrated how GIS and remote sensing can be used to quantify real-world environmental changes. By calculating the lake’s surface area over time, users can see the impact of factors like water management and climate change. The hands-on workflow, clear instructions, and real-world application made it an effective learning experience that strengthened skills in imagery analysis, raster classification, and spatial problem-solving.
Week 12 - Calculate Impervious Surface Area
The ArcGIS Learn project Calculate Impervious Surfaces from Spectral Imagery helped a lot in understanding how imagery can be used to analyze urban environments. The tutorial walks step-by-step through the process of using multispectral imagery to identify and measure impervious surfaces such as roads, rooftops, and parking lots. It clearly explains how to prepare the data, segment imagery into meaningful groups, and perform a supervised classification, making the workflow approachable even for beginners.
One of the most helpful aspects of this project is learning how spectral signatures are used to distinguish different land cover types. By grouping pixels into segments and applying a supervised classification with training samples, the tutorial shows how to accurately separate pervious surfaces (like vegetation and soil) from impervious surfaces (like concrete and asphalt). This process improves classification accuracy and demonstrates how remote sensing data can be transformed into meaningful, real-world information.
Overall, this tutorial was beneficial because it demonstrated how GIS can be used to quantify environmental impacts such as urban runoff and flooding risk. By calculating the amount of impervious surface area within each land parcel, users can support real-world decision-making, such as stormwater management and urban planning. The clear instructions, hands-on workflow, and practical application made this an effective and valuable learning experience that strengthened both imagery analysis and GIS skills.
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