Blog - Remote Sensing

In a fast paced and always changing global economy the ability to classify crop fields via remote sensing at the end of a growth cycle does not provide the much needed immediate insight required by decision makers. To address this problem we developed a model that allows continuous classification of crop fields at any point in time and improves predictions as more data becomes available. In practice, we developed a single model capable of delivering predictions about which crops are growing at any point in time based on satellite data. The data available at the time of inference could be a few images at the beginning of the year or a full time series of images from a complete growing cycle. This exceeds the capabilities of current deep learning solutions that either only offer predictions at the end of the growing cycle or have to use multiple models that are specialized to return results from pre-specified points in time. This article details the key changes we employed to the model described in a previous blog post “Classification of Crop fields through Satellite Image Time Series” that enlarges its functionality and performance. The results presented in this article are based on a research paper recently published by dida. For more detailed information about this topic and other experiments on this model please check out the original manuscript: “Early Crop Classification via Multi-Modal Satellite Data Fusion and Temporal Attention” .

An estimated number of 906 Earth observation satellites are currently in orbit, providing science and industry with many terabytes of data every day. The satellites operate with both radar as well as optical sensors and cover different spectral ranges with varying spectral, spatial, and temporal resolutions. Due to this broad spectrum of geospatial data, it is possible to find new applications for remote sensing methods in many industrial and governmental institutions. On our website, you can find some projects in which we have successfully used satellite data and possible use cases of remote sensing methods for various industries . Well-known satellite systems and programs include Sentinel-1 (radar) and Sentinel-2 (optical) from ESA, Landsat (optical) from NASA, TerraSAR-X and TanDEM-X (both radar) from DLR, and PlanetScope (optical) from Planet. There are basically two types of geospatial data: raster data and vector data . Raster data Raster data are a grid of regularly spaced pixels, where each pixel is associated with a geographic location, and are represented as a matrix. The pixel values depend on the type of information that is stored, e.g., brightness values for digital images or temperature values for thermal images. The size of the pixels also determines the spatial resolution of the raster. Geospatial raster data are thus used to represent satellite imagery. Raster images usually contain several bands or channels, e.g. a red, green, and blue channel. In satellite data, there are also often infrared and/or ultraviolet bands. Vector data Vector data represent geographic features on the earth's surface, such as cities, country borders, roads, bodies of water, property rights, etc.. Such features are represented by one or more connected vertices, where a vertex defines a position in space by x-, y- and z-values. A single vertex is a point, multiple connected vertices are a line, and multiple (>3) connected and closed vertices are called polygons. The x-, y-, and z-values are always related to the corresponding coordinate reference system (CRS) that is stored in vector files as meta information. The most common file formats for vector data are GeoJSON, KML, and SHAPEFILE. In order to process and analyze these data, various tools are required. In the following, I will present the tools we at dida have had the best experience with and which are regularly used in our remote sensing projects. I present the tools one by one, grouped into the following sections: Requesting satellite data EOBrowser Sentinelsat Sentinelhub Processing raster data Rasterio Pyproj SNAP pyroSAR Rioxarray Processing vector data Shapely Python-geojson Geojson.io Geopandas Fiona Providing geospatial data QGIS GeoServer Leafmap Processing meteorological satellite data Wetterdienst Wradlib

The field of remote sensing has been benefiting from the advancements made in Machine Learning (ML). In this article we explore a state of the art model architecture, the Transformer , initially developed for Natural Language Processing (NLP) problems, which is now widely used with many forms of sequential data. Following the paper by Garnot et al. , we utilize an altered version of this architecture to classify crop fields from time series of satellite images . With this, we achieve better results than traditional methods (e. g. random forests) and with less resources than recurrent networks.

In this blog article, I want to briefly describe the process of migrating labels from Planet Scope to Sentinel-2 images. I will explain the process step-by-step and provide the necessary lines of code. The code can easily be adapted to migrate labels between other satellites than in our example case here.

Unsurprisingly, a major requirement that makes mining endeavours successful is the right location - one where the enterprise knows with confidence that the soil bears high grade minerals of interest. Finding such a site, however, poses a significant challenge. Conventionally, when mining enterprises pursue greenfield exploration, field studies and drillings are conducted. As these are very expensive, they should only serve as a last assurance after potentially interesting regions have been identified. This is where Remote Sensing comes into play. In this article, we will have a look at the possibilities that spaceborne imaging provides for greenfield exploration. Let’s have a satellite scout promising spots.

Working with satellite data , one needs to understand and possibly convert the coordinates the data is given in. Sometimes, especially if released by official bodies, satellite data is provided in MGRS tiles , which are derived from the UTM coordinate system. For example, this is true for Sentinel-2 tiles. I want to answer the following three questions in this post, using the Python libraries mgrs and pyproj : What is the difference between MGRS and UTM? To which MGRS tile does a certain point referenced in latitude and longitude degrees belong to? How can I express a MGRS tile in Lat/Lon coordinates? Before we answer these questions, let's first look into what MGRS is.

Urbanisation on a global scale is happening at an ever increasing rate. In the year 2008, more than 50% of the worlds population lived in cities and it is predicted that by 2050 about 64% of the developing world and 86% of the developed world will be urbanised. This trend puts significant stress on infrastructure planning. Providing everything from sanitation, water systems and transportation to adequate housing for more than 1.1 billion new urbanites over the next 10 years will be an extraordinary challenge. In a research project for the European Space Agency's program "AI for social impact", dida assessed the use of state-of-the-art computer vision methods for monitoring urban development over time of three rapidly growing cities in west Africa: Lagos, Accra and Luanda. The population of these cities are expected to grow by 30-55% in size by the end of 2030 which means that in-situ data collection about how these cities develop is almost impossible given the available resources. Instead, we came up with a concept that would rely solely on satellite images and machine learning.

Throughout the globe, rain forests and other natural landscapes are endangered by illegal mining, which transforms areas formerly rich in flora and fauna into wasteland. In order for local governments to take countermeasures, they first need to know about the locations of illegal mines. In countries covered by vast areas of impenetrable rain forest, such as Brazil or Congo, obtaining this information is a difficult problem. In this blog post I describe an approach to detect illegal mines based on deep learning and remote sensing, that we have developed to support the conservation efforts of governments and NGOs. In particular, we use a U-Net for semantic segmentation , a branch of computer vision. As part of the project of automatic detection of illegal mines , we were also joined by scientists from the Institute of Mineral Resources Engineering of the RWTH Aachen University, who contributed their mining-specific expertise. The project was funded by the European Space Agency .

In this blog post I will describe a number of pretraining tasks one can use either separately or in combination to get good “starting” weights before you train a model on your actual labelled dataset. Typically, remote sensing tasks come under the umbrella of semantic segmentation , so all the pretraining techniques described here are for tasks that output a prediction for each pixel and use a U-Net as the architecture.

Here I’m going to walk through how we approached the problem of detecting convective clouds in satellite data including what we are looking for (and why!) and the machine learning approach we used. This post will consist of four sections: First we will introduce convective clouds and give a brief overview of the problem. In section 2 we will discuss the satellite data we are working with. In section 3 we discuss how we go about manually labelling the data, which is a particularly difficult task requiring the use of some external data. Finally, in section 4 we will give a brief overview of the neural network architecture that we use, the U-Net, and how we go about training it. You can also have a look at my talk at 2020's Applied Machine Learning Days in Lausanne, Switzerland:

This post presents some key learnings from our project on identifying roofs on satellite images . Our aim was to develop a planing tool for the placement of solar panels on roofs. For this purpose we set up a machine learning model that accurately partitions those images into different types of roof parts and background. We learned that the UNet model with dice loss enforced with a pixel weighting strategy outperforms cross entropy based loss functions by a significant margin in semantic segmentation of satellite images. The following idealized pipeline illustrates the functionality of the planning tool: