In my research, I combine methods from the fields of machine learning and deep learning with multimodal remote sensing data.
This results in applications that make it possible to systematically extract information from large datasets and make it usable for interdisciplinary problems.
I place particular focus on the efficiency of the methods used, allowing them to be trained and applied on relatively small datasets and with simple computing infrastructure.
Below, I show examples from my research:
Commercial vehicle traffic is currently responsible for 7% of global CO2 emissions. While road freight will remain the dominant mode of surface freight transportation, its contribution to climate change is likely to increase in the short term. Therefore, the quantitative monitoring of commercial vehicle (CV) traffic is essential for implementing targeted road emission regulations. However, ground monitoring stations are costly and less than half of all countries worldwide collect road freight activity. In this work, we investigate the feasibility of detecting and monitoring CV traffic in freely available satellite imagery from ESA’s Sentinel-2 satellites.
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The industrial and power-generating economic sectors emit more than half of the annually and globally released greenhouse gas emissions, strongly contributing to global warming effects. In our recent work (Mommert et al. 2020) we laid the foundation to estimating greenhouse gas emissions from industrial sites by characterizing industrial smoke plumes from remote imaging data only. That work is part of a bigger effort to estimate greenhouse gas emission rates from satellite imagery for individual industrial sites. In this work, we made one further step towards achieving this goal.
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The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth’s climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA’s Sentinel-2 satellites.
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Don Quixote was discovered in 1983 and although it has a perfectly comet-like orbit, no cometary activity (a tail or a coma around the object) have been observed. According to protocol, the object was listed as an asteroid.
In 2009, we found from observations with the now defunct Spitzer Space Telescope that there actually is a faint coma around Don Quixote that most likely consists of molecular band emission from either CO or CO2. This is discussed in detail here and in this paper.
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Motivation Most (optical) telescopes have to be protected from precipitation to prevent damage to their optics and electronics. For this reason, most observatories use all-sky cameras - cheap, but very sensitive CMOS cameras equipped with fish-eye lenses - that monitor the night sky for incoming clouds. Telescope operators can observe live streams from these cameras to make informed decisions as to whether the current weather situation requires the closing of the telescope dome, or not.
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Ever since the early days of my PhD thesis, I have been involved in the thermal modeling of thermal-infrared observations of near-Earth objects and other asteroids. Such asteroid thermal models simulate the surface temperature distributions of asteroids utilizing some simplifying assumptions and enabling the estimation of physical properties like size and albedo.
In the framework of several large-scale Spitzer Space Telescope observing programs, we have observed more than 2000 near-Earth objects and measured their thermal emission to constrain their diameters and albedos. Now that Spitzer has officially retired, we have finalized our observations and analysis, and compiled all of our results in one large data set. This compilation comprises 2204 diameter and geometric albedo estimates for 2132 different near-Earth objects - the largest homogeneous data set of its kind for this population. The results of these efforts are available GitHub.
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TESS is the Transiting Exoplanet Survey Satellite, which was launched into space by NASA in 2018 to identify faint brightness variations in stars that are characteristic of planets orbiting around and transiting in front of these stars.
A rendering of the TESS spacecraft. The instrument consists of four imaging cameras, each covering 24° x 24° of the sky. Over the course of its nominal two-year mission, TESS will monitor more than 200,000 stars with the photometric accuracy necessary to identify exoplanet transits. In order to observe this huge number of stars, TESS points at different “Sectors” in the sky, amounting to a total of 85% of the entire sky. Each Sector spanning 24° x 96° is observed over a period of about one month.
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The ESA mission Gaia not only observes a good fraction of the stars in the milky way, but also a huge number of asteroids. Gaia Data Release 2 (DR2) is the first data release to include a number of asteroid observations, limited to G magnitudes (no color information) and a pre-selected sample of 14099 asteroids from different populations. For each asteroids, DR2 contains a median number of 9 observations over the first 9 months of the mission. While this limited data set does not allow for deriving the targets’ rotational periods or constraining their taxonomic types, it is useful for a look into the shape distribution of asteroids.
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The vast majority of all asteroid diameters and albedos that are currently available have been derived from thermal infrared observations using a method called thermal modeling. Thermal models simulate the surface temperature distribution on an asteroid which is used to derive the thermal infrared flux that is emitted by the body. By varying the model parameters - mainly its diameter and albedo - the properties of an asteroid can be fit to observations of the thermal emission and hence estimated.
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sbpy is an astropy affiliated package that will provide tools for small-body planetary astronomers. The idea is provide a collection of well-tested and documented tools that asteroid and comet observers use on a daily basis. The goal is to improve the reproducibility of results and to make it easier for (young) researchers to try out new ideas.
The development of sbpy has just started, as our NASA PDART grant just recently arrived. A lot of work to be done, but a few glimpses of its usefulness can already be seen.
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