As a Researcher (formerly Junior Group Leader) at the Institute for Interdisciplinary Mountain Research within the Austrian Academy of Sciences in Innsbruck, Austria, I bring a wealth of expertise to multiple fields, including Geostatistics, remote sensing, and Machine Learning. Prior to my current role, I was an Assistant Professor in Geo-Environmental Data Science at Utrecht University and a Postdoctoral Fellow at Stanford University. My PhD from the University of Lausanne was focused on Geostatistics, which remains a primary area of my research.
My work involves the use of Multiple Point Statistics (MPS) to simulate complex structures, while also integrating remote sensing techniques such as Google Earth Engine (for which I am a recognized Google Developer Expert) and High-Performance Computing (HPC) to advance geoscience research. I have a broad range of scientific interests and am dedicated to utilizing cutting-edge technologies in my research.
Currently, my research is focused on developing a novel passive sensing technology based on photogrammetry that can generate a digital elevation model (DEM) from a single flyover. Additionally, I am actively engaged in collaborative projects covering various subjects, including remote sensing, geostatistics, and bird tracking.
As a Researcher (formerly Junior Group Leader) at the Institute for Interdisciplinary Mountain Research within the Austrian Academy of Sciences in Innsbruck, Austria, I bring a wealth of expertise to multiple fields, including Geostatistics, remote sensing, and Machine Learning. Prior to my current role, I was an Assistant Professor in Geo-Environmental Data Science at Utrecht University and a Postdoctoral Fellow at Stanford University. My PhD from the University of Lausanne was focused on Geostatistics, which remains a primary area of my research.
My work involves the use of Multiple Point Statistics (MPS) to simulate complex structures, while also integrating remote sensing techniques such as Google Earth Engine (for which I am a recognized Google Developer Expert) and High-Performance Computing (HPC) to advance geoscience research. I have a broad range of scientific interests and am dedicated to utilizing cutting-edge technologies in my research.
Currently, my research is focused on developing a novel passive sensing technology based on photogrammetry that can generate a digital elevation model (DEM) from a single flyover. Additionally, I am actively engaged in collaborative projects covering various subjects, including remote sensing, geostatistics, and bird tracking.
I always keep an older picture, to fit with other researchers!
Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.
My research using Machine Learning is mainly focus around geoscience and how to use such ML framework in unconventional context.
My research in geostatistics mainly focuses on statistical simulations. More precisely, to develop new algorithms in Multiple Point Statistics (MPS) and Machine Leraning to generate complex structure.
Remote sensing had a huge impact on my career and remains important to me. Currently, most of my research in remote sensing is done through collaborations on different projects from Vegetation evolution (Vietnam and Valais), growing season, frozen lakes and even NPP (net primary productivity) evolution in ocean. Currently, most of hese studies are done on online platforms such as Google Earth Engine, which enable studies through a gigantic amount of data.
I put particular importance on investigation cutting-edge solutions, from both a technological as well as algorithmic point of view. Most applications of these solutions are done through collaborations.
Furthermore, I put particular effort and time into allowing the results of various studies to be available to the community though functional, open and easy-to-use software or library.
The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex world wide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and to write.
The main output of this project is QuickSampling (QS),a training image-based simulation tool that is a free, open and can be furthure developpe. It uses a Direct Sampling approach that was enhanced using FFT for speed and ranking (as opposed to the threshold) that tends to be less sensitive. All this results in a more efficient and robust algorithm. The tool is available through G2S. The current implementation handles continuous, categorical, multivariate and gaped datasets.
The main output of this project is an algorithm - Narrow Distribution Selection (NDS) - to spectrally enhance remotely sensed satellite images. This algorithm uses a similar approach" to QuikSampling to automatically and statistically colorize the image. It uses a pair of images with a high and low spectral resolution in addition to the image to enhance. The algorithm is free, open, modifiable and available through G2S toolset.
The main goal of this approach is to propose an alternative to calibrate algorithms such as QucikSampling or Direct Sampling. Based on the analysis of the training image and possibility to reproduce patterns, that algorithm will provide an optimal and evolving calibration, that reduces as much as possible verbatim copy. This approach does NOT rely on complex objective function, and is therefore much more versatile.
This study explores the effects and the errors introduced by the change of spatial resolution in remote sensing applications.
FastDS enhances Direct Sampling by taking advantage of the first few pixels to remove, for the list of potential candidates, all patterns that trivially would not match. Going from random sampling to predicted potential candidate is comparable to going from a naive and uniform rejection sampling to an optimal adaptive rejection sampling. This approach provides a significant boost compared to traditional DS, without introducing measurable bias.
Studing the evolution of snow and vegetation over the last 35+ years.
Cheetah evolved from Impala and SNESIM. The idea is to encode a k-class n-point patten in k n-bit numbers. The hope is that the method will work nicely, even with just a minor performance improvement. Mathematically, the 3 algorithms are equivalent and therefore provide similar simulations. Currently, investigating the use of FPGA can bring a true breakthrough in performance and power consumption.
MAZAlib is a project to provide a tool - algorithm implementation and interface - to easily do segmentation of porous media scans.
The goal of this project is to track the type and the growth of the vegetation in Vietnam.
The project produced a high-frequency and high-resolution image time series of the Gornergletscher (in the Swiss Alps) derived from repeated UAV surveys. I implemented the tracking algorithm.
The Open Earth Engine Library (aka. OEEL) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with free and open algorithms.
This project relies on pressure instead of light to determine the position of birds during migration.
The GeoStatistical Server (G2S) is a framework that allows you to use state-of-the-art Multiple Point Statistics (MPS) algorithms to run stochastic simulations. G2S is designed to run simulations in a generic way, independently of the code used or language it is written in. For example, it enables to run a C/C++ simulation code using Python, or Python using MATLAB (or any other combination). It includes QucikSampling, Narrow Distribution Selection (NDS) and autoQS. Furthermore, it can easily be extended to handle any simulation grid-based simulation algorithms.
The Open Earth Engine Library (aka. OEEL) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with free and open algorithms.
The Open Earth Engine extension (aka. OEEex) is part of the open-geocomputing initiative. The goal is to provide Google Earth Engine (GEE) users with a dedicated chrome extension to enhance their experience.
A quick and interactive study to evaluate the time needed to correct gender bias as a function of the duration of a career. The code here includes an example with an academic career, but the equations are general and can be directly used in any field."
Few functions to compute nD variograms and 2D cumulants with matlab.
A header only library to find the k smallest/largest values (and their index) in an unsorted array. These functions are unbiased. In case of multiple positions with the same value, all are recorded if under k, otherwise the positions are sampled accordingly. These functions were designed for k <<< N (the size of the array). Furthermore, functions were implemented with intrinsic functions to get optimal performance (similar to searching for extremes). A Matlab wrapper is provided in this repository.
Implementation in C/C++ of 2DDW, check the original publication for more information.
I didn't develop the method, I only implemented a C/C++ version.
A QGIS interface for G2S. It allows running remote sensing stochastic simulations directly in QGIS.
This is a simple code in python to grasp how Direct Sampling and QucikSampling are working. It was intentionally reduced to its roots, the goal is to get the key component and not the performance. This code is a multiple order of magnitude slower than the C/C++ implementations.
Do pattern matching between big images (50k x 50k). Extremely useful to follow slow objects such as glaciers over time, that can be deformed.
Fast Gaussian Simulation (FGS) is a Matlab function which generates multiple n-D Gaussian Field very quickly. It uses the Fast Fourier Transform (FFT). It removes the edge effect on long-range, but introduces a micro bias for shorter ranges (only if the range outside of the simulation size is used).
A set of training images useful for MPS. Saved as TIFFF, it allows the user to load these images directly from Github in single or few lines. Therefore, it's perfect for use as demonstration code, where there is no need to send a separate training set.
Server that returns the probability of position of a bird based on pressure record using Google Earth Engine to access massive ERA-5 dataset.
Language | Level |
---|---|
Google Earth Engine (subset of JavaScript/Python) | |
C / C++ / OpenMP | Advance expert |
MATLAB | Advance expert |
Python | Advance expert |
TensorFlow | Advance expert |
CUDA / OpenGL / CL | Expert |
Shell / Bash | Expert |
Emscripten | Expert |
Javascript / JQuery / HTML / CSS | Expert |
Markdown | Expert |
Latex | Intermediate |
R | Intermediate |
Maple | Intermediate |
Java | Intermediate |
SQL | Intermediate |
OWL/SPARQL | Intermediate |
MATLAB | <==> | Python |
Python | <==> | C/C++ |
MATLAB | <==> | C/C++ |
Name |
---|
Sublime Text |
Git |
Illustrator |
Office |
Cinema4D |
QGIS |
... |
Language | Level |
---|---|
French | +++ |
English | ++ |
German | + |
I am always eager to mentor motivated students. However, it is essential that you have a co-supervisor who is a Professor within your own academic institution.
Statistical simulations are extremely useful to get realisation of random processes. In the case of use of transfer functions (forward simulations) realisations are required to compute accurate estimations. Over the 30 last year’s Multiple Point Statistics (MPS) changed the landscape of geostatistical simulations, by providing realistic samples of complexly structured processes. Unfortunately, these methods are currently restricted to pixel (raster) based simulation. However, a number of situations are not gridded.
Another approach like object-based simulation can be used for non-pixel-based situation, however, these approaches are limited to simple structure only.
This master project as for goal to experiment to develop a sequential simulation approach based on objects instead of pixels, but conserving the pattern matching process that is at the origin of the success of MPS.
Application of this newly developed algorithm would mainly be on object base class simulation.
During early 1900s, reforestation effort took place over vast areas of Europe, but lands were converted to monoculture plantations of pine and spruce in Mediterranean and Temperate regions, respectively, rather than to forests. Furthermore, those tree monoculture plantations occurred in the wrong place, posing a series of environmental issues that call for the need of assessing the evolution of those patches during the last century. This project aims at developing an understanding of “reforested” patch dynamics over time. The objectives are to implement remote sensing tools using Google Earth Engine for detecting and measuring reforested patches in terms of both land cover and forest type.
The life cycle of plants is controlled by temperature, changing throughout the season. Robust evidence indicates that the increase of temperature due to fossil fuel extraction and combustion is shifting plant’s life cycle changes by prompting earlier growth in spring. However, little is known about whether and how other key components of the life cycle are changing too including senescence. It looks reasonable to hypothesize that global warming is making trees losing their leaves later and later. This project aims at developing an understanding of how the ‘conclusion’ plant life cycle is influenced by rising temperatures. The objectives are to implement remote sensing tools using Google Earth Engine for detecting and measuring when and where plants change the date of loosing their leaves. This is a challanging topic that will require time series analysis, multispectal data, and even maybe ML. Understanding the impact of global warming on plant senescence is key for biogeochemical cycling and ecosystem functioning.
The state of lakes (frozen /unfrozen) is critical for multiple processes. On of this key process is the evaporation that is directly related to it. To quantify the impact of the climate change on freezing lakes, we need to be able to determine the periods on which lakes are frozen. This task can be solved using passive optical sensing; However, such sensing remains limited due to high clouds cover during freezing periods.
In this project we propose to use Sentinel 1 (SAR) data, that is known to sense trough cloud coverage. Using SAR data to detect lake status rise various challenges such as how to handle Sun reflection on the surface water or wave on the water.
The project will be focusing on overcoming these challenges, by solving the Sun position geometric equation, and using ML to properly detect the status of lakes.
Nowadays, more satellites are observing earth than ever before, but each with a different sensor, and each one has different characteristics. Different spectral alignment methods exist (form simple equation to advance Machine Learning technics), each with its own pros and cons. A common approach is to approximate the bias using a simple linear regression over enough data points. This can be applied to the spectral band itself or to derivative products (e.g., NDVI, EVI, and other indexes). If an extensive literature exists about such transformation, these studies are restricted to recent sensor (mainly NASA Landsat 7-8 and ESA Sentinel 2), and assume a universal transformation.
This project focuses on the idea that such transformation should be determined for archive imagery too (Landsat TM and MSS at least). Especially today, these early data are crucial to study earth evolution, and therefore need to be comparable to today’s acquired data. Furthermore, the spatial variability of such transformation should be evaluated, in particular regarding land cover. In fact, the overabundance of particular landcovers tends to bias the computed correction.