Internet of Things (IoT)
In the course "From Sensors to Maps: Working with IoT and Geodata," you'll learn how this entire process works. Over the course of two days, you'll build your own IoT sensor that collects measurement data, enriches it with geolocation information, and publishes it as GeoJSON for use in geospatial applications
In our increasingly connected world, sensors and smart devices play a key role in collecting information about the physical environment. The Internet of Things (IoT) enables devices, sensors, and systems to connect with each other via the internet. This allows real-world measurements to be automatically collected, processed, and shared. When these sensor measurements are linked to location data, a powerful source of geodata is created that can be used for analyses, maps, and dashboards.
IoT technology forms the basis of many modern applications, such as smart cities, environmental monitoring, infrastructure management, and digital twins. For example, sensors measure temperature, air quality, noise, or soil moisture and send this data via the internet to systems where the information can be stored, analyzed, and visualized.
In this course, you will learn how this chain—from sensor measurement to geodata—actually works. You will build your own IoT sensor that collects measurement data and makes this data available via the internet as GeoJSON, so that the data can be used directly in maps and geo-applications.
The course is designed to be practical and accessible. No prior knowledge of electronics or embedded systems is required.
The Internet of Things consists of a network of devices that use sensors to collect information and share this data via the internet. The core of an IoT system usually consists of three components: sensors that take measurements, a device that processes the data, and a system where the data is stored and made available for further analysis.
In many IoT solutions, a microcontroller plays a central role. This is a small programmable device that can read and process sensor data. Combined with a network connection, for example via Wi-Fi, the device can send this data to a server or cloud environment.
When sensor data is combined with geolocation, for example via a GPS module, a dataset is created in which each measurement is directly linked to a location. This allows sensor measurements to be visualized on maps or used for geographic analysis.
An important component of modern geo- and IoT systems is the use of open standards for data exchange. In this course, we will work with JSON and GeoJSON, widely used formats for exchanging geodata via web APIs.
By structuring sensor data in this way, the information can be easily used in dashboards, GIS systems, or web maps.
IoT technology is playing an increasingly important role in the world of geoinformation. Whereas datasets were often collected manually or measured periodically in the past, IoT sensors make it possible to continuously and automatically collect data from the physical world.
This technology is applied in a wide range of sectors, such as:
Linking sensor measurements to location creates a rich source of real-time geodata. This data can be used for monitoring, analysis, and decision-making. In many modern systems, IoT therefore serves as a key building block for digital platforms, dashboards, and even digital twins of cities or infrastructure.
The course demonstrates how this technology works in practice and how sensor measurements ultimately become part of a comprehensive geodata infrastructure.
In this course, you will learn how to program a microcontroller and how to connect sensors to collect measurement data. You will work with a compact and affordable microcontroller that is specifically suited for IoT applications.
You’ll learn how sensors generate data and how this data is read and processed by a microcontroller. You’ll also be introduced to the basic principles of embedded programming in C++.
Through these practical exercises, you’ll gain insight into how IoT devices function in practice and how sensor data is collected.
An important part of the course is linking sensor measurements to location data. By using a GPS module, each measurement can automatically be tagged with latitude and longitude.
The sensor data is then structured as JSON and sent via Wi-Fi to a backend. There, the data is stored in a database and made available via an API.
The measurements are ultimately published as GeoJSON, allowing them to be used directly in maps and GIS applications.
This way, you’ll experience how a complete IoT data flow works: from sensor measurement to geodata visible on a map.
During the course, you’ll gain insight into the entire technical chain behind modern IoT systems. You’ll see how sensors, microcontrollers, web APIs, and databases work together to convert data from the physical world into usable geodata.
The system’s backend runs in a container environment and is built using Python (FastAPI) and PostgreSQL/PostGIS. This also gives you insight into how modern data infrastructures are structured.
The final result of the course is a working prototype: an IoT sensor that takes measurements, automatically determines its location, and sends this data to a server where the information becomes available as geodata.
Taking this course offers a number of key benefits for professionals working with geodata, technology, or data infrastructures.
Practical approach
You’ll build an IoT sensor yourself and go through the entire process from sensor measurement to geodata.
Combination of IoT and geoinformation
The course focuses specifically on the link between sensor technology and geographic data.
Low barrier to entry
No prior knowledge of electronics or embedded systems is required.
Insight into modern data architectures
You will learn how sensor data is made available for maps and analyses via APIs, databases, and web standards.
By the end of the course, you will have a solid understanding of how IoT systems work and how sensor measurements can be converted into usable geodata for maps, dashboards, and analyses.
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Contact usDay 1 focuses on the basics: understanding how a microcontroller works and how sensors are connected and read.
We’ll start with an introduction to IoT devices, sensors, and microcontrollers. Then we’ll set up the development environment and write our first programs in C++.
Topics for Day 1:
By the end of the day, you’ll understand how a microcontroller works, how to connect sensors, and how to program an IoT device.
On the second day, we’ll expand the system into a full-fledged geosensor.
We’ll connect a GPS module to the microcontroller so that each measurement is automatically assigned a geolocation. Then we’ll organize the data and send it to a backend via Wi-Fi.
Topics for Day 2:
We’ll show how this data appears in the backend and how sensor measurements become part of an IoT data stream to the cloud or database.
The end result of Day 2 is a working prototype: an IoT sensor that takes measurements, determines its own geolocation, and sends the data as GeoJSON geodata to a server.
By the end of the course "From Sensors to Maps":
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
In IoT applications, sensor data is often enriched with location data using a GPS module or other positioning technology. This assigns a latitude and longitude coordinate to each measurement. When this data is stored or transmitted, the location can be used immediately to visualize the measurements on a map or analyze them within GIS systems. This makes it possible to interpret sensor data spatially and, for example, identify patterns or hotspots.
GeoJSON is a widely used format for exchanging geographic data via web APIs. It combines geometry (such as points or lines) with attributes in a single JSON structure. This makes it easy to share sensor data with web maps, GIS software, and dashboards. Because many modern geo-applications directly support GeoJSON, it is an efficient format for visualizing real-time sensor measurements on maps.
A backend ensures that sensor data from devices can be received, stored, and made available to other applications. In a typical IoT architecture, a sensor sends measurements to a server or API. The backend processes this data, stores it in a database, and makes it accessible for maps, dashboards, or analytics. This creates a complete data chain from sensor measurement to geo-application.
Linking sensors to maps has many practical applications. Examples include environmental monitoring—such as measuring air quality or temperature—smart city applications like traffic or noise monitoring, and agricultural projects that track soil moisture or microclimates. Visualizing sensor data on maps provides a spatial overview that aids in analysis, monitoring, and decision-making.
