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Found 16 papers, 0 papers with code
A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models
The widespread use of black-box AI models has raised the need for algorithms and methods that explain the decisions made by these models.
Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times
The results demonstrate that ITX outperforms all baseline methods by at least 5% (equivalent to $70, 000 for a 6, 000 vehicle operation) per week in terms of a monetary reward function which was modeled to replicate the profitability of a real-world ridepooling system.
Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images
Despite the plethora of successful Super-Resolution Reconstruction (SRR) models applied to natural images, their application to remote sensing imagery tends to produce poor results.
Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning
Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem.
EscapeWildFire: Assisting People to Escape Wildfires in Real-Time
Over the past couple of decades, the number of wildfires and area of land burned around the world has been steadily increasing, partly due to climatic changes and global warming.
The pursuit of beauty: Converting image labels to meaningful vectors
A challenge of the computer vision community is to understand the semantics of an image, in order to allow image reconstruction based on existing high-level features or to better analyze (semi-)labelled datasets.
Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning
Natural forests are complex ecosystems whose tree species distribution and their ecosystem functions are still not well understood.
Can animal manure be used to increase soil organic carbon stocks in the Mediterranean as a mitigation climate change strategy?
In this paper, a simulation is performed using the area of Catalonia, Spain as a case study for the characteristic low SOC in the Mediterranean, to examine whether animal manure can improve substantially the SOC of agricultural fields, when applied as organic fertilizers.
Transfer of Manure as Fertilizer from Livestock Farms to Crop Fields: The Case of Catalonia
Intensive livestock production might have a negative environmental impact, by producing large amounts of animal manure, which, if not properly managed, can contaminate nearby water bodies with nutrient excess.
Transfer of Manure from Livestock Farms to Crop Fields as Fertilizer using an Ant Inspired Approach
Intensive livestock production might have a negative environmental impact, by producing large amounts of animal excrements, which, if not properly managed, can contaminate nearby water bodies with nutrient excess.
Characterizing Halloumi cheese bacterial communities through metagenomic analysis
Eighteen samples made by different milk mixtures and produced in different areas of the country were analyzed, to reveal that Halloumi microbiome was mainly comprised by lactic acid bacteria (LAB), including Lactobacillus, Leuconostoc, and Pediococcus, as well as halophilic bacteria, such as Marinilactibacillus and Halomonas.
Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques
However, the performance of predicting in- and outflux is less sensitive to the prediction horizon.
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles
This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV).
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment.
Deep learning in agriculture: A survey
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential.
Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning
Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest.
