Eagle Service User Manual

1. Introduction to the Eagle Service 

1.1. Service Overview: The Earth AI Gateway 

Welcome to the Destination Earth Eagle service, the Geospatial Processing Platform. Eagle is designed as the “Earth AI Gateway for Land Evaluation,” a specialized tool for professional geospatial and environmental analysis.

As illustrated by the service’s main welcome screen, the platform’s core purpose is to harness the power of satellite imagery and advanced Artificial Intelligence (AI) models. It is optimized to analyze land suitability for specific, high-impact applications, including agriculture and photovoltaic (solar panel) installations. The service provides a streamlined interface to process complex geospatial data with efficiency and ease.

Fig 1: The Eagle Platform Welcome Screen

1.2. Core Platform Capabilities 

The Eagle service is built upon a clear, three-stage conceptual methodology, which is visualized on the platform’s welcome screen (Fig 1). This “data-to-report” workflow forms the basis of all tasks performed within the service:

Satellite Analysis: The platform provides access to and processes real-time satellite imagery and measurements, serving as the foundational data source for analysis.
AI Models: Users leverage a suite of advanced sustainability analysis and predictive models. These AI models are the “engine” of the service, transforming raw satellite data into specialized, thematic insights.
Detailed Reports: The final output of an analysis is a comprehensive report. This includes statistical summaries, key metrics, and visual results that can be used for decision-making, reporting, and further research.

This user manual is structured to guide a new user through the practical application of this three-stage process.

1.3. Understanding the Project-Based Workflow 

To use the Eagle platform effectively, it is essential to first understand its organizational hierarchy. All work within Eagle is managed through a “Project-Based Workflow.” This structure is composed of two primary levels:

Project: A Project is the highest-level container. It is created to organize a specific initiative, research question, or broad geographical region (e.g., “Amazon Deforestation Analysis,” “European Solar Assessment”).
Process: A Process is a specific analysis task run within a Project. A single Project can contain multiple Processes. For example, a “Water” project (see Section 5.3) could contain numerous individual processes for “Water Detection” run on different dates or for different sub-regions.

This hierarchical model (User -> Project -> Process) is fundamental to the platform’s design. As this manual will demonstrate, the user’s first action upon entering the platform is not to run an analysis, but to create a Project to serve as a container for that analysis.

2. The “My Projects” Dashboard 

2.1. The User Dashboard: Your Central Hub 

Upon successful authentication, the user is directed to the “My Projects” dashboard. This screen serves as the central hub and main landing page for all activities on the Eagle platform.

All project management and analysis workflows begin from this interface.

2.2. Navigating the Dashboard Interface 

The “My Projects” dashboard interface is clean and function-oriented. The primary navigation elements are:

Platform Header: “Destination Earth EAGLE” is displayed at the top, identifying the service.
Primary Action Button: The “+ Create New Project” button is the main call to action on this screen.
Navigation Links: “My Projects” and “Dashboard” links are provided for navigation.
User Identifier: The account identifier of the currently logged-in user is displayed in the lower portion of the screen.

2.3. The “Empty State” Dashboard 

For all new users, the “My Projects” dashboard will first appear in its “empty state,” as depicted in Fig 2.

The "My Projects" Dashboard in its empty state — Fig 2: The “My Projects” Dashboard in its empty state

This screen presents the message: “No projects yet. Create your first project to get started!”. This UI element is an important instructional cue, as it reinforces the project-based workflow established in Section 1.3. It actively guides the new user to their first and only available action: creating a new project.

3. Managing Your Analysis Projects 

3.1. Procedure: Creating a New Project 

The first functional step for any user on the Eagle platform is to create a project. This procedure is initiated from the “My Projects” dashboard.

Click the + Create New Project button, as shown in Fig 2.
The “Create New Project” modal window will appear, as shown in Fig 3.

The "Create New Project" modal with blank fields — Fig 3: The “Create New Project” modal with blank fields

This modal contains two required fields for defining the project:
- Project Name: A descriptive title for the project container.
- Description: A brief summary of the project’s goals and scope.
Complete these fields with the relevant project details. Fig 4 shows an example of a completed form with “Test” as the Project Name and “Test project” as the Description.

The "Create New Project" modal with example data entered — Fig 4: The “Create New Project” modal with example data entered

Click the Create Project button to save the new project and return to the dashboard.

3.2. The Populated Project Dashboard 

After the first project is created, the “My Projects” dashboard (Fig 5) is now populated. The empty state message is replaced by one or more “Project Cards,” with each card representing a single project.

Upon creation, a dismissible tip may appear to guide the user to the next logical step: “Tip: Click on ‘Manage Processes’ button on any project card to create and manage land analysis processes.”. This UI element provides a clear and direct signpost, indicating that the user should now move from project management to process execution.

The populated "My Projects" dashboard showing a project card — Fig 5: The populated “My Projects” dashboard showing a project card

3.3. Understanding the Project Card Interface 

Each project on the dashboard is represented by a Project Card (Fig 5). This card serves as a summary and an entry point for the project’s analysis processes. The key elements of the card are:

Project Name: The title of the project (e.g., “Test”).
Project Description: The user-provided description (e.g., “Test project”).
Creation Date: The date the project was created (e.g., “Created: 27/10/2025”).
Process Count: A numerical indicator (e.g., “2”) showing how many analysis processes are contained within that project.
Manage Processes Button: This is the primary action button on the card. Clicking this button navigates the user into the project’s specific “Process History” interface, which is the gateway to the core analysis workflow.

3.4. Project Management Options 

In addition to creating projects, the “My Projects” dashboard is the central location for ongoing project management. Each project card includes controls for project maintenance. While not explicitly depicted in Fig 5, these options typically include:

Edit Project (Pencil Icon): Allows the user to modify the Project Name and Description.
Delete Project (Trash Icon): Permanently removes the project and all associated analysis processes.

4. The Geospatial Analysis Workflow 

This section details the core functionality of the Eagle service: the multi-step workflow for executing a geospatial analysis.

4.1. The “Process History” Interface 

After clicking the “Manage Processes” button on a specific project card (see Fig 5), the user is navigated to the “Process History” screen for that project, as shown in Fig 6.

This interface is the “home base” for all analysis within the selected project. It retains the project’s context (e.g., “Test” and “Test project”) and provides a “Back to Projects” link for navigation.

Similar to the main dashboard, this screen has an “empty state” for new projects, which displays the message: “No processes yet. Create your first process to analyze land areas.”. The primary action button on this screen is + Create Process, which launches the analysis wizard.

The "Process History" screen in its empty state — Fig 6: The “Process History” screen in its empty state

4.2. Procedure: Initiating a New Analysis 

The end-to-end analysis workflow is initiated by clicking the + Create Process button from the “Process History” screen. This action launches a multi-step analysis wizard that guides the user through configuration.

4.3. Step 1: Defining Your Area of Interest (AOI)

4.3.1. Selecting the “Draw New Area” Method 

The first step of the analysis wizard is to “Choose how to define your area of interest” (Fig 7). This step requires the user to specify the geographic boundaries for the analysis.

The platform provides an option to “Draw New Area,” which is described as allowing the user to “Create a new area by drawing on the map.”.

The "Draw New Area" method selection — Fig 7: The “Draw New Area” method selection

4.3.2. Using the Interactive Map Interface 

Selecting the “Draw New Area” option navigates the user to an interactive map interface (Fig 8) where the AOI polygon can be drawn.

The interface provides explicit instructions on “How to draw:”:

Click on the map to add points
Double-click the large orange circle to close the polygon (need 3+ points)
Or double-click anywhere to finish

Controls are also available to “Reset drawing” or navigate “Back” to the previous step.

Fig 8: The map drawing interface and instructions

4.4. Step 2: Selecting an Analysis Model 

4.4.1. Overview of Available Models 

After a valid AOI is defined, the wizard automatically proceeds to the “Select Analysis Model” step (Fig 9). The UI provides confirmation that the previous step was successful: “Area Selected. Your area has been drawn successfully.”.

This step is the core of the Eagle service, where the user selects the specific AI model to apply to their AOI. The available models are presented in a list:

Semantic Segmentation: “Classify land cover into categories (vegetation, water, buildings, etc.)”
Change Detection: “Detect changes between time periods (deforestation, urbanization, etc.)”
Solar Panel Detection: “Identify existing solar panel installations in the area”
Water Detection: “Detect and map water bodies (lakes, rivers, wetlands, floods)”

The "Select Analysis Model" list — Fig 9: The “Select Analysis Model” list

4.4.2. Table 1: Eagle Analysis Model Capabilities 

Model	Description
Semantic Segmentation	Classify land cover into categories (vegetation, water, etc.)
Change Detection	Detect changes between time periods
Solar Panel Detection	Identify existing solar panel installations
Water Detection	Detect and map water bodies (lakes, rivers, wetlands, floods)

4.5. Step 3: Configuring Analysis Parameters 

4.5.1. Model-Specific Parameter Configuration 

After an analysis model is selected, the UI dynamically displays any additional, model-specific parameters required to run the process.

Fig 10 illustrates this step. After selecting “Solar Panel Detection” (inferred), a new section, “Select Analysis Date,” appears. This field is mandatory, as indicated by the placeholder text and the disabled “Run Process” button. This guided workflow prevents the user from submitting an incomplete request.

Fig 10: Model-specific parameter (Analysis Date) with no date selected

4.5.2. Finalizing and Running the Process 

The final step before execution is to complete all mandatory fields. Fig 11 shows the fully configured process:

Model Selected: Solar Panel Detection
Analysis Date Selected: “01/10/2025”

The interface provides a confirmation of the date format that will be used by the system: “Will use: 20251001.”. With all parameters set, the ▷ Run Process button is now enabled (illuminated in blue), indicating the process is ready to be executed. Clicking this button submits the job to the platform.

The "Run Process" button enabled after all parameters are set — Fig 11: The “Run Process” button enabled after all parameters are set

4.6. Step 4: Executing and Monitoring the Process 

Once the process is submitted, the user is shown a status screen (Fig 12). This screen provides feedback that the job is active: “Processing your analysis… This may take a few seconds.”.

5. Reviewing, Interpreting, and Exporting Results 

This section covers the final and most critical stage of the workflow: accessing, interpreting, and exporting the analysis results.

5.1. Accessing and Reviewing Completed Processes 

After the processing (Fig 12) is complete, the user is automatically returned to the “Process History” screen for the project. This screen, previously empty in Fig 6, is now populated with a “process card” for the completed job (Fig 13).

This card provides an at-a-glance summary of the job:

Status: “Ready” (indicating completion and success)
Model Name: “Solar Panel Detection”
Process ID: A unique identifier (e.g., “#10”)
Date: “27/10/2025”
Action: A “View Results” button to access the report.

The "Process History" with a completed process — Fig 13: The “Process History” with a completed process

5.2. Use Case 1: Solar Panel Detection 

The following demonstrates the results workflow using a Solar Panel Detection analysis.

5.2.1. Interpreting the Solar Panel Detection Results Page 

Clicking the “View Results” button (from Fig 13) navigates the user to the “Solar Panel Detection Results” page (Fig 14).

This report page displays all outputs from the analysis. It includes:

Process Metadata: “Process #10. Test 27/10/2025…”
Results Visualization: An interactive map displaying the geographic location of the detected panels within the AOI.
Key Metrics: A high-level statistical summary.
Detailed List: A granular, itemized list of detected objects.
Export Function: A prominent Export Report (PDF) button.

The "Solar Panel Detection Results" page — Fig 14: The “Solar Panel Detection Results” page

5.2.2. Analysis of Key Metrics (Solar)

The “Key Metrics” and “Summary” panels on the results page (Fig 14) provide the primary, actionable intelligence for a professional user. The metrics shown are:

Detected Panel Areas: The total count of discrete solar panel arrays identified by the model.
Total Panel Area: The cumulative surface area of all detected panel arrays.
Total Capacity: This is a critical inferred metric. The AI model estimates the potential power generation capacity based on the detected panel area. For a renewable energy planner, this provides an immediate, quantitative assessment of existing generation in the AOI.
Avg Confidence: This represents the AI model’s average certainty across all detections. This metric is a crucial indicator of the report’s overall data quality and reliability.

5.2.3. Analyzing Individual Detected Panel Data 

Beyond the high-level summary, the results page (Fig 14) provides a granular, itemized list of “Detected Solar Panels” for detailed inspection.

5.3. Use Case 2: Water Detection 

The Eagle platform is a multi-project, multi-model service. The following use case demonstrates running a different analysis (“Water Detection”) in a separate project, reinforcing the standard workflow.

5.3.1. Procedure: Creating a New Project for a Different Analysis 

A user is not limited to a single project. From the “My Projects” dashboard, new projects can be created at any time to organize different initiatives.

Fig 15 shows the creation of a second project (“Test 2”), and Fig 16 shows the creation of a third project named “Water” with the description “Water detection.”. This demonstrates the platform’s capability to manage multiple, concurrent, and unrelated analysis projects.

Creating a second project ("Test 2") — Fig 15: Creating a second project (“Test 2”)

Creating a third project ("Water") — Fig 16: Creating a third project (“Water”)

5.3.2. Reinforcing the Standard Analysis Workflow 

Once inside the new “Water” project, the analysis workflow is identical to the one described in Section 4. This consistency is a key design feature of the Eagle service.

Step 1: Define AOI: The user clicks “+ Create Process” and is presented with the map drawing interface (Fig 17) to define the AOI.
Step 2 & 3: Select Model & Parameters: The user selects the “Water Detection” model and provides the required “Analysis Date” (e.g., “27/10/2025”). The “Run Process” button is enabled, and the job is submitted (Fig 18).

This demonstrates that the AOI Definition -> Model Selection -> Parameter Configuration wizard is the standard, repeatable procedure for all analysis types on the platform.

Fig 17: Drawing an AOI for the Water Detection process

Selecting the "Water Detection" model and date — Fig 18: Selecting the “Water Detection” model and date

5.3.3. Accessing Water Detection Process Results 

As with the first use case, the completed job appears in its project’s “Process History” (Fig 19). The process card confirms the job is “Ready” and identifies the model as “Water Detection” (Process ID: #24), with the “View Results” button available.

The "Water" project "Process History" with a completed job — Fig 19: The “Water” project “Process History” with a completed job

5.3.4. Interpreting the Water Detection Results Page 

Clicking “View Results” opens the “Water Detection Results” page (Fig 20). This report page is specific to the water model but maintains the standard layout: metadata, a map visualization of detected water bodies, key metrics, and the “Export Report (PDF)” button.

5.3.5. Analysis of Key Metrics (Water)

The “Key Metrics” for the Water Detection model (Fig 20) are different from the solar panel model, demonstrating the specialized nature of each analysis. The metrics provided are:

Total Water Area: The cumulative surface area of all detected water bodies within the AOI.
Water Coverage: The percentage of the user’s defined AOI that is covered by water. This is a critical statistic for flood plain analysis, disaster response (e.g., determining the percentage of an area that is inundated), or agricultural water resource management.
Water Bodies: The total count of discrete water bodies (lakes, rivers, reservoirs, etc.) identified.
Avg Confidence: The AI model’s average certainty for this analysis.

5.4. Exporting Your Analysis Report 

The final, and most critical, function on any results page is exporting the report. As seen in both the Solar Panel Detection (Fig 14) and Water Detection (Fig 20) results, a prominent Export Report (PDF) button is available.

This action allows the user to download a persistent, shareable, and printable document containing the maps, key metrics, and detailed data from their analysis, allowing the results to be used for external reporting and decision-making.

6. Support and Contact 

For any technical issues, questions regarding analysis results, or to provide feedback on the Eagle service, please use the “Support” link located in the main platform navigation bar.

Complete the contact form with all relevant details, and a member of the Destination Earth support team will provide assistance.