Yacine Bouroubi

• PhD
• Principal Investigator at Effigis - Montreal

Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annot...

Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture....

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentat...

The Bidirectional Reflectance Distribution Function (BRDF) defines the anisotropy of surface reflectance and plays a fundamental role in many remote sensing applications. This study proposes a new machine learning-based model for characterizing the BRDF. The model integrates the capability of Radiative Transfer Models (RTMs) to generate simulated r...

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution g...

Segmentation and classification are two imperative, yet challenging tasks in image analysis for remote-sensing applications. In the former, an image is divided into spatially continuous, disjoint, and homogeneous regions, called clusters, in terms of their various properties: shape, intensity, texture, colour, contrast, etc. Classification, on the...

Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a class, object or feature, to each point. This research uses ConvPoint, a deep learning method, to perform airborne point cloud classification at scale, in r...

A collaborative initiative between remote sensing researchers, web platform developers, artificial intelligence experts and satellite imagery providers. Implement a web platform that allows users to make annotations on VHR images (e.g.: Pleiades 50 cm; WorldView-2 40 cm; WorldView-3 30 cm). Create large datasets of annotations and related patches....

This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC...

Dredging may cause the recirculation of contaminated suspended sediments in the water column and lead to their movement to aquaculture sites. Effigis, in collaboration with Public Works and Government Services Canada and the Canadian Space Agency, has developed a methodology for monitoring the concentration of offshore suspended sediment (CSS) usin...

Various generative and discriminative methods have been transferred from the computer vision field to remote sensing applications using different low and high semantic level descriptors. However, as classical approaches have shown their limits in representation learning and are not intended to deal with the great variability of the data. With the e...

Global warming due to climate change has become an issue of great concern worldwide, especially for countries suffering from direct impacts and facing great risks of damage from climate change like Vietnam. Reducing and phasing out fossil fuels together with promoting, investing in and developing alternative green energy sources such are important...

La géomatique et l’agriculture de précision permettent d’adapter la gestion des parcelles agricoles en fonction de la variabilité intra-champ en modulant les interventions ou les traitements selon les besoins et selon la variabilité spatiotemporelle des propriétés du sol et des cultures. Elle vise à développer de bonnes pratiques agricoles permetta...

The last three decades have seen significant mining development in the northern regions of Canada, where the freeze and thaw cycle of permafrost and corresponding surface subsidence and heave represent a significant challenge at all mining stages, from the design of infrastructures to the monitoring of restored areas. Over the past ten years, SAR i...

This paper presents three applications of SAR interferometry. In the first study, DInSAR technique applied to TerraSAR-X images acquired in Nunavik, northern Canada, showed that surface subsidence observed in the permafrost thaw period is more important in loose soil areas while no surface movement were detected in rock outcrop areas. In the second...

Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information...

Information-based crop management, such as variable rate technology, allows for changing rates of fertilization according to local needs. Fertilizer prescription maps can be derived from crop growth status assessed through proximal canopy sensing technologies. Typically, a vegetation index, such as the NDVI, is used to estimate a Nitrogen Sufficien...

The response of corn yield to in-season nitrogen rate (ISNR) fertilizer applications in a temperate humid climate is conditioned to a great extent by prevailing weather-conditions, which affect nitrogen use efficiency and raise the level of uncertainty for making management decisions. A better understanding of the effects of temperature, expressed...

Soil properties and weather conditions are known to affect soil N availability and plant N uptake; however, studies examining N response as affected by soil and weather sometimes give conflicting results. Meta-analysis is a statistical method for estimating treatment effects in a series of experiments to explain the sources of heterogeneity. In thi...

A fuzzy Inference System (FIS) was developed to generate recommendations for spatially variable applications of nitrogen (N) fertilizer using soil, plant and precipitation information. Experiments were conducted over three seasons (2005-2007) to assess the effects of soil electrical conductivity (ECa), nitrogen sufficiency index (NSI), and precipit...

Nitrogen (N) fertilizers are often applied to maize (Zea mays L.) in excess of economically optimal rates because of the uncertainty of dealing with seasonal and spatial variability. A better understanding of the relationships among field, apparent soil electrical conductivity (ECa), elevation, slope and seasonal characteristics is therefore essent...

Nitrogen (N) is the most important nutrient in agriculture since it is critical to achieve quantity and quality of harvest. However, excess of N fertilization is costly and can have negative impacts on crops and the environment. The NDVI can be used as a valuable indicator of crop performance and can be translated into a Nitrogen Sufficiency Index...

Fuzzy logic inference systems (FISs) can help provide within-eld nitrogen (N) fertilization recommendations by combining critical plant-and soil-based spatial information. This chapter describes how, based on spatially distributed information, FIS can be used to develop in-season N recommendations. A sample problem is provided. Soil and plant infor...

A better understanding of weather and terrain effects on corn (Zea mays L.) growth and nitrogen (N) response is essential to determine optimal variable-rate N applications. This study was conducted in 2010 on a 0.6 ha field where different N treatments were crossed by a sprinkler irrigation line to generate a spatially variable water supply (WS). A...

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250kgNha−1 conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (...

Fuzzy logic inference systems (FISs) can help provide with-in field nitrogen (N) fertilization recommendations by combining critical plant- and soil-based spatial information. This chapter describes how, based on spatially distributed information, FIS can be used to develop in-season N recommendations. A sample problem is provided. Soil and plant i...

Multi-spectral satellite imagery, especially at high spatial resolution (finer than 30 m on the ground), represents an invaluable source of information for decision making in various domains in connection with natural resources management, environment preservation or urban planning and management. The mapping scales may range from local (finer reso...

Hourly global solar irradiation data useful for the design of solar energy conversion systems is generated using a new satellite based model called SICIC (solar irradiation from cloud image classification). It is a model built by processing high resolution visible Meteosat images and ground measurements of solar radiation flux collected in various...

Field corn experiments were conducted over the years 2004 to 2006 inclusive in the Montérégie area of the province of Quebec, Canada. Terrain features were characterized and crop vegetation index were monitored. At mid-growth stage, V9-VT, a Duncantech multispectral digital camera with 0.25 m resolution was performed in 2004 and a hyperspectral CAS...

Hourly global solar radiation flux incident on an inclined surface is evaluated in any site of Algeria using monthly mean daily sunshine duration measurements. The methodology used consists of successive transformations of solar data, respectively, based on the exponential probability distribution of daily sunshine duration, Ångström equation, beta...

A method of smoothing solar data by beta probability distributions is implemented in this paper. In the first step, this method has been used to process daily sunshine duration data recorded at thirty-three meteorological stations in Algeria for eleven year periods or more. In the second step, it has been applied to hourly global solar irradiation...

This chapter deals with the analysis of seasonal variations of sunshine duration measured in fifty four meteorological stations of Algeria. The sunshine duration data have been processed on a monthly basis using the Fourier analysis. In the North of Algeria and High Tablelands, the seasonal variations of sunshine duration are conveniently represent...

The object of this work is the presentation of an approach for the estimate of global irradiation, by clear sky and average sky, applicable to the whole of the Algerian territory. Approach which takes account of the nature of the Algerian network of measurements, characterised by a low density of radiometric stations (7 stations) distributed on a s...

The REFLECT software package was developed by the Remote Sensing Laboratory of the University of Montreal. REFLECT allows the transformation of the numerical values of the spectral bands from satellite images into ground reflectances. It is based on the 6S atmospheric code for the computation of atmospheric parameters, on the dark objects method to...