Riccardo La Grassa- PhD
- Researcher at National Institute of Astrophysics
Riccardo La Grassa
- PhD
- Researcher at National Institute of Astrophysics
About
41
Publications
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Introduction
Current institution
Publications
Publications (41)
Fine-Grained classification models can expressly focus on the relevant details useful to distinguish highly similar classes typically when the intra-class variance is high and the inter-class variance is low given a dataset. Most of these models use part annotations as bounding box, location part, text attributes to enhance the performance of class...
In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, t...
In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DT...
The impact crater detection offers a great scientific contribution in analyzing the geological processes, morphologies and physical properties of the celestial bodies and plays a crucial role in potential future landing sites. The huge amount of craters requires automated detection algorithms, and considering the low spatial resolution provided by...
We release a new global catalog of impact craters on the Moon containing about 5 million craters. Such catalog was derived using a deep learning model, which is based on increasing the spatial image resolution, allowing crater detection down to sizes as small as 0.4 km. Therefore, this database includes ~69.3% craters with diameter lower than 1 km....
Advanced navigation capabilities are essential for precise landing operations, enabling access to critical lunar sites and supporting future lunar infrastructure. To achieve accurate positioning, innovative navigation methods leveraging neural network frameworks are being developed to detect distinctive lunar surface features, such as craters, from...
Context . The absolute flux calibration of a planetary camera is pivotal for a quantitative analysis of the brightness that is reflected by a celestial body to a) characterise its microphysical properties, b) analyse changes caused by exogenic or endogenic activity, and c) produce high-quality image mosaics to understand the geology of the body. Th...
Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time Series (SITS). However, the emergence of transformer networks in various vision tasks raises the question of whether they can outperform CNNs in this task as well. This paper presents a revised version of the T...
Recent studies have shown that Convolutional Neural Networks (CNNs) achieve impressive results in crop segmentation of Satellite Image Time Series (SITS). However, the emergence of transformer
networks in various vision tasks raises the question of whether they can outperform CNNs in this task as well. This paper presents a revised version of the T...
Weeds are a crucial threat to agriculture, and in order to preserve crop productivity, spreading agrochemicals is a common practice with a potential negative impact on the environment. Methods that can support intelligent application are needed. Therefore, identification and mapping is a critical step in performing site-specific weed management. Un...
An accurate, frequently updated, automatic and reproducible mapping procedure to identify seasonal cultivated crops is a prerequisite for many crop monitoring activities. Deep learning was demonstrated to be an effective mapping approach already successfully applied to decametric resolution satellite images (like Sentinel-2 data) to produce yearly...
Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset ob...
![Algorithm results compared with [34] on Food.com dataset.](publication/359464789/figure/tbl1/AS:1144395105607680@1649856463501/Algorithm-results-compared-with-34-on-Foodcom-dataset_Q320.jpg)
Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food recommendation scheme, mappin...
Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve better performance. In this paper, we designed a transfer learning methodology that combi...
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to r...
In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neural networks (CNN) use the loss function as a supervisory signal to train a deep model and contribute significantly to achieving the state of the art in some fields of arti...
In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving th...
![Four images extracted from VQA-Med 2019 [7] dataset (synpic371,...](publication/349145460/figure/fig4/AS:989362665832448@1612893849018/Four-images-extracted-from-VQA-Med-2019-7-dataset-synpic371-synpic10103-synpic16486_Q320.jpg)
A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture f...
In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on “where and when” crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are...
Optimization methods are of great importance for the efficient training of neural networks. There are many articles in the literature that propose particular variants of existing optimizers. In our article, we propose the use of the combination of two very different optimizers that, when used simultaneously, can exceed the performance of the single...
The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word em...
Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compare neural models trained with the use of different optimizers. Each paper demonstrates that for a particular problem an optimizer is better than the others but as the prob...
In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neural networks (CNN) use the loss function as a supervisory signal to train a deep model and contribute significantly to achieving the state of the art in some fields of arti...
In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neural networks (CNN) use the loss function as a supervisory signal to train a deep model and contribute significantly to achieving the state of the art in some fields of arti...
A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as problems of hierarchical classification, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for...
The transfer learning technique is widely used to learning in one context and applying it to another, i.e. the capacity to apply acquired knowledge and skills to new situations. But is it possible to transfer the learning from a deep neural network to a weaker neural network? Is it possible to improve the performance of a weak neural network using...
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to misclassification problems. Thi...
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and graph-based model based on Minimum Spanning Tree (MST). In a novelty detection scenario, the training data is no polluted by outliers (abnormal class) and the goal is to r...
In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during the training phase using an ensemble of spanning trees. It takes the best classifier, partitioning the area near a pattern into $\gamma^{\gamma-2}$ sub-spaces and combining...
Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension possible. Convolutional Neural Networks (CNN) are an excellent tool for recognizing and classifying text documents. In addition, it can generate images conditioned on natura...
One-class classifiers are trained only with target class samples. Intuitively, their conservative modeling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods leveraging on the combination of one-class classifiers based on non-...
One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods are proposed which leverage on the combination of one-class classif...
Questions
Questions (3)
Dear colleagues,
I am guest editor for the Special Issue “Advancements in Deep Learning for Remote Sensing: Exploring Planetary and Earth Observation Applications” in Remote Sensing. We invite original research articles, reviews, and case studies that apply or advance deep learning methods for challenges in both planetary science (e.g., terrain characterization, crater detection, geological feature analysis) and Earth observation (e.g., land cover change detection, agricultural monitoring, environmental and hazard assessment).
Key Details
- Submission deadline: 29 August 2025 (ongoing publication upon acceptance) mdpi.com.
- Submission portal: Register and submit via the MDPI Remote Sensing submission system; use the Special Issue page for direct access mdpi.com.
Scope Highlights
- Development or adaptation of deep learning architectures (e.g., CNNs, transformers, self-/semi-supervised approaches, domain adaptation) for remote sensing tasks.
- Integration of multi-source data (optical, radar, LiDAR, hyperspectral) and strategies for limited-label scenarios (data augmentation, synthetic data).
- Applications in planetary exploration (e.g., Mars, Moon, other bodies) and Earth observation (e.g., precision farming, urban monitoring, disaster response).
- Emphasis on interpretability, uncertainty quantification, and scalable processing pipelines (cloud/high-performance computing).
Benefits
- Enhanced visibility through a thematic collection, dedicated promotion, and potential reprint in MDPI books.
- Interdisciplinary networking among AI, planetary science, and geospatial communities.
- Opportunity to showcase methodological innovations with practical impact on remote sensing.
We look forward to your contributions.
Dr. Riccardo La Grassa and Dr. Ignazio Gallo
Guest Editors, Remote Sensing
Hello everyone,
as the title suggest, do you know any metric to express numerically using for example a score to evaluate the difference between two features visualization techniques in Computer vision field?
Given two images processed with GradCam or gradient-based approach, is it possible to evaluates two samples?
For example, a sample output extracted by Resnet-18 is very different from a sample extracted by another model, and so on.
Thanks a lot for eventually knowledge sharing.
I'm searching for a numerical dataset about the virus.
I found only daily statistical data but i would like access to single patients data.
Does anyone know about it?
