52°North Spatial Information Research GmbH

Mentors: Student: ShashaankS

The 52°North Weather Routing Tool (WRT) computes fuel-optimal maritime routes but is entirely configuration-file-driven, making it inaccessible to non-developer users. This project delivers a QGIS plugin that solves this problem. A six-page guided wizard covers route setup with map-click waypoint entry, vessel parameters, weather and depth dataset upload with validation, algorithm selection, and constraint configuration - exporting a ready-to-run JSON config. A dockable weather panel renders NetCDF variables as time-animated layers with spatial subset statistics. A route visualization module displays WRT output with per-waypoint fuel consumption popups and distance calculation. An optional component runs the WRT directly from QGIS via an isolated Python venv, streaming live progress to the canvas. Deliverables: installable QGIS plugin, all four components and full documentation.

Mentors: Student: Pranav Angrish

OGC APIs — Features, Records, EDR, and Processes — are powerful but inaccessible to non-developers. This project builds a formal MCP mapping specification that translates OGC API operations into structured tools any LLM can use, enabling urban planners and researchers to query geospatial data and chain complex workflows using plain English. Deliverables: a language-agnostic JSON mapping spec covering all four OGC API types, a Python reference implementation, and a browser-native showcase requiring no GIS expertise.

Mentors: Student: shreyass-ranganatha

The Weather Routing Tool is a system designed to find optimal shipping routes between two geographic points, taking into account weather data, local boundaries, and other environmental factors. The tool currently assumes constant ship speed and has a basic genetic algorithm for route optimization. This project aims to improve the routing logic by: Introducing ship speed optimization along a route Enhancing the genetic algorithm for more effective route modeling Improving the usability and extensibility of the tool for end-users Project Deliverables 1. Ship Speed Optimization Goal: Allow optimization of ship speed along the route instead of assuming it constant. Approach: Modify the Isofuel algorithm to use speed as a minimization criterion. Define a new scoring function: score = (distance / time) - alpha * remaining_distance Enable this feature via configuration: ISOCHRONE_MINIMISATION_CRITERION = "speed" 2. Genetic Algorithm Enhancements Goal: Improve the existing genetic algorithm’s efficiency and flexibility. Planned Improvements: Add multi-objective cost function support (e.g., fuel, speed, wear & tear). Enhance crossover logic with: Multi-point crossover Uniform crossover Adaptive crossover Improve mutation strategies and population initialization. Leverage the pymoo library with custom configurations. 3. Tool Usability Improvements Goal: Make the Weather Routing Tool more modular, user-friendly, and extensible. Key Features: Dynamic Python file injection using CLI: python cli.py -f config.template.json custom.py Load modules dynamically with: load_file_as_module(path: str) -> types.ModuleType Command-line utility packaging: Tool installable via PyPI (pip install wrt) Usage: wrt -h Rationale: Enable users to add custom boat models and configurations without modifying core code.

Mentors: Student: goyalpranjal_

The KomMonitor web client, a tool for geospatial data monitoring in urban planning, is currently based on AngularJS and requires a complete migration to the modern Angular framework to improve maintainability, scalability, and ease future contributions. While migration started previously, significant parts remain in AngularJS, and foundational issues like specific AngularJS patterns ($rootScope, $compile) and JavaScript files hinder the direct use of standard upgrade tools.   Solution: This project aims to fully migrate the KomMonitor web client from AngularJS to Angular. The approach involves:   1. Addressing the foundational roadblocks identified by analysis tools (e.g., refactoring $rootScope usage, eliminating $compile, converting JavaScript to TypeScript).   2. Incrementally migrating remaining AngularJS components and services to Angular, leveraging ngUpgrade within a hybrid architecture during the transition.   3. Implementing modern Angular best practices, including modular architecture, dependency injection, and potentially lazy loading.   4. Refactoring components using design patterns like Observer (RxJS), Facade, and Container-Presenter to improve structure and data handling.   5. Ensuring UI consistency using ng-bootstrap.   6. Establishing a robust testing strategy encompassing unit, integration, and end-to-end tests using frameworks like Jest and Cypress.   Deliverables: 1. A fully migrated KomMonitor web client codebase, transitioned from AngularJS to Angular.   2. Resolution of key AngularJS patterns ($rootScope, $compile) and conversion of necessary JavaScript files to TypeScript.   3. Migrated and optimized components/services adhering to modern Angular standards and design patterns.   4. A comprehensive test suite ensuring application stability and functionality post-migration.   5. Updated documentation reflecting the migrated codebase.   6. Final project code, documentation, and evaluation report.

Mentors: Student: Humaid Kidwai

OGC SensorThings API is an international standard to eliminate vendor lock-ins in IoT systems and to create an open geospatial ecosystem by defining a standard data model and describe how to retrieve the ingested data. However, the standard by itself does not specify a way to physically store the data. Most of the current server implementations are all based on Postgres databases using the PostGIS extension. Unfortunately, relational databases are not a great choice to store large volumes of data, which is often the case with IoT applications. As a result, retrieving large volumes of data from any of the open source SensorThings API servers is painfully slow. Modern data lakehouse standards and cloud-native geospatial file formats offer a scalable, modular, cost-effective and a much faster way to store and work with large volumes of geospatial data on the web. Specifically, Apache Iceberg is an open table format for organizing data lakes in object stores and ensuring ACID guarantees. GeoParquet is a cloud-native geospatial file encoding, based on a columnar data storage format for tabular data that significantly compresses the data and improves querying efficiency. A cloud native SensorThings API extension using Apache Iceberg could significantly enhance the standard's ability to ingest and aggregate large heterogeneous streams of sensor data. The proposal hence puts forth a design architecture for any SensorThings API server to use Iceberg to store and retrieve sensor data in a more efficient manner reducing memory overhead and network latencies when retrieving such data over the web. Eventually, as GeoParquet gets merged into Iceberg, the implementation will support a much faster alternative to existing SensorThings API implementations for handling sensor data at scale.