Remote sensing, AI and innovative prediction methods for adapting cities to the impacts of the climate change

Urban areas are not only one of the biggest contributors to climate change, but also they are one of the most vulnerable areas with high populations who would together experience the negative impacts. In this paper, I address some of the opportunities brought by satellite remote sensing imaging and artificial intelligence (AI) in order to measure climate adaptation of cities automatically. I propose an AI-based framework which might be useful for extracting indicators from remote sensing images and might help with predictive estimation of future states of these climate adaptation related indicators. When such models become more robust and used in real-life applications, they might help decision makers and early responders to choose the best actions to sustain the wellbeing of society, natural resources and biodiversity. I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods and the predictive estimation models in general

Using Local Features to Measure Land Development in Urban Regions

Monitoring urban development in a given region provides valuable information to researchers. Currently available, very high resolution satellite images can be used for this purpose. However, manually monitoring land development using these large and complex images is time consuming and prone to errors. To handle this problem, an automated system is needed to measure development in urban regions. Therefore, in this study we propose such an automated method to measure land development in a given urban region imaged in different times. We benefit from novel land development measures for this purpose. They are based on local features obtained from sequential images. As a novel contribution, we represent these local features in a spatial voting matrix. Then, we propose five different land development measures on the formed voting matrix. We test our method on 19 sets of sequential panchromatic Ikonos images. Our test results indicate the possible use of our method in measuring land development automatically

Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learning algorithms are not sample-efficient and requires long training times and quantities of data. In this work, we proposed a framework for sample-efficient Reinforcement Learning that take advantage of state and action representations to transform a high-dimensional problem into a low-dimensional one. Moreover, we seek to find the optimal policy mapping latent states to latent actions. Because now the policy is learned on abstract representations, we enforce, using auxiliary loss functions, the lifting of such policy to the original problem domain. Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy

A System for Efficient Path Planning and Target Assignment for Robotic Swarms in Agriculture

With a growing world population, the demand for food grows accordingly, putting agricultural production under continuous pressure for more productivity. However, traditional farming approaches are reaching their limits, due to constraints in money and human effort. A solution to this problem lies in automation via the use of emerging technologies, which can accelerate procedures and operations without the need for additional human resources. Suitable modern technologies for various tasks of precision agriculture include the use of the Internet of Things (IoT) via lightweight IoT-enabled robotic systems, which can form swarms for operating in parallel. Path planning for these robotic swarms is a complex, NP-hard problem. This paper addresses the problem of efficient path planning of robotic swarms, formulating the problem as a specific type of Vehicle Routing Problem (VRP). Various state-of-the-art algorithms are employed to solve this VRP, in order to decide on the best approach for different agricultural topologies, tasks, and number of robots available. An end-to-end system is proposed and evaluated, using the Internet/Web as an infrastructure and communication medium, taking GPS input data from map providers, identifying and applying the most suitable algorithm for the specific landscape and task; and finally producing GPS coordinates as routes for the robots to follow. Recommendations for further improvements are discussed.</p

On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach

We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information. The planner is trained using a reward function shaped based on the online knowledge of the map of the training environment, obtained using grid-based Rao-Blackwellized particle filter, in an attempt to enhance the obstacle awareness of the agent. The agent is trained in a complex simulated environment and evaluated in two unseen ones. We show that the policy trained using the introduced reward function not only outperforms standard reward functions in terms of convergence speed, by a reduction of 36.9\% of the iteration steps, and reduction of the collision samples, but it also drastically improves the behaviour of the agent in unseen environments, respectively by 23\% in a simpler workspace and by 45\% in a more clustered one. Furthermore, the policy trained in the simulation environment can be directly and successfully transferred to the real robot. A video of our experiments can be found at: https://youtu.be/UEV7W6e6Zq