Seminars – 2022-2023

  • Date: March, 16th
  • Speaker: Ana di Toro (PhD. candidate, Unicamp, remote sensing scientist Regrow, São Paulo, Brasil)
  • Room: D070 (bat Coppens)
  • Title: SAR and Optical data applied to Early season Mapping Integrated Crop-Livestock systems
  • Abstract: Regenerative agricultural practices are a suitable path to feed the global population since those practices tend to reverse climate change, increase crop production by restoring soil biodiversity and increase soil organic matter. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. In this context, there is a lack of knowledge about ICLS fields and how to identify and monitor them. In this seminar, after giving an overview of the ICLS systems, I will show the results achieved using three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. Also, considering the high incidence of cloud cover in Brazil, we tested and compared SAR and Optical time series, in two different study sites. Finally, I will explain the next steps and remaining challenges.
  • Date: February, 2d
  • Speaker:  Lynn Miller (PhD. candidate, Monash Univ., Australia)
  • Room:  D009 (bat ENSIBS)
  • Title: Deep learning from SITS for predicting live fuel moisture content
  • Abstract: Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for wildfire high risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data, with recent studies exploring the use of both deep learning models and satellite image time series (SITS) data. However, almost all these models estimate current LFMC (i.e., they are nowcasting models). Models able to make accurate predictions of LFMC in advance (projection models) would provide fire management authorities with more timely information for assessing and preparing for wildfire risk. In this seminar I will discuss our work designing and evaluating a deep learning model to predict LFMC across the continental United States 3-months in advance. This is the first model that can make wide-scale long-range predictions while achieving an accuracy close to that of nowcasting models. The model consists of a small ensemble of temporal convolutional neural networks created using readily available inputs. I will also talk about some of the challenges of using machine learning to predict LFMC and potential ways of addressing these challenges.
  • Date: January, 26th
  • Speaker: Matteo Ciotola (PhD. candidate, Università degli Studi di Napoli)
  • Room: D001 (bat ENSIBS)
  • Title: Pansharpening by Convolutional Neural Networks
  • Abstract: Pansharpening is a fusion process which combines a lower-resolution multispectral image with a higher-resolution panchromatic band to provide a high-resolution multispectral image.
    There has been a growing interest in deep learning-based pansharpening in recent years. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. One of the problems is the absence of ground truths, necessary items for supervised pansharpening models. This is often addressed by training networks in a reduced-resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full-resolution images, results are often below expectations, suggesting the violation of this assumption. In this presentation, it will be explored a new training scheme for pansharpening networks operating on full-resolution, real, data. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus preventing possible mismatches between the simulated, lower-resolution, training datasets and the real, full-resolution, test datasets. To prove the effectiveness of the proposed framework, different networks and datasets have been used for experimental validation, achieving consistent and high-quality results.
  • Date: January, 12th
  • Speaker: Johan Faouzi (ass. prof. in Computer Sciences,  ENSAI, Rennes)
  • Room: D001 (bat ENSIBS)
  • Title: Time series classification: A review of algorithms and implementations
  • Abstract: Many algorithms on time series classification have been published in the literature. From dynamic time warping to shapelets to images to convolutions, a wide variety of approaches have been investigated. However, as more and more algorithms are published, using and comparing may be more and more cumbersome for users. In this presentation, I will highlight the main approaches that have been investigated to tackle time series classification. Finally, I will briefly present open source software tools that allow for using these algorithms in a user-friendly way, including a Python package that I created and still maintain.
  • Date: December, 8th
  • Speaker: Mathieu Le Lain (INFO dpt, IUT Vannes, univ. Bretagne Sud)
  • Room: D001 (Bat. ENSIBS)
  • Title: Classification of Halpha lines for Be stars by neural networks
  • Abstract: A database of Be stars spectra has been implemented 15 years ago by the Observatoire de Paris-Meudon in order to collect astronomical spectra made by amateur and professional astronomers. In order to analyze the different states and potentially predict the next bursts of stars, this work focuses on the classification of Halpha line shapes as a GADF graph from residual neural networks. After having discussed the context and the objectives, we will detail the steps of this work, its implementation in application form and the future steps.
  • Date: November, 3rd
  • Speaker: Martina Pastorino (PhD student, INRIA Nice)
  • Room: Amphi Y. Coppens
  • Title: Stochastic models and deep-learning methods for remote sensing image analysis
  • Abstract: Recent advances in DL, especially deep convolutional neural networks, have made it possible to obtain very significant results in the field of remote sensing image analysis. However, as for other methods, the map accuracy depends on the quantity and quality of ground truth (GT) used to train them. Having densely annotated data (i.e., a detailed, pixel-level GT) allows obtaining effective models, but requires high efforts in annotation. GTs related to real applications, such as remote sensing, are almost never exhaustive, they are spatially sparse and typically do not represent the spatial boundaries between the classes. Models trained with sparse maps usually produce results with poor geometric fidelity. This significantly affects the accuracy of the classification, and it is a major challenge in the development of deep neural networks for remote sensing. At the same time, PGMs have sparked even more interest in the past few years, because of the ever-growing availability of VHR data and the correspondingly increasing need for structured predictions. The objective of this research work is to combine different ideas from these approaches (deep learning and stochastic models) to develop novel methods for remote sensing image classification. The logic is to take advantage of the spatial modeling capabilities of hierarchical PGMs to mitigate the impact of incomplete GTs and obtain accurate classification results. In particular, the study focuses on the possibility of exploiting the intrinsically multiscale nature of FCNs, to integrate them with hierarchical Markov models.

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