Search

This paper is concerned with gradual land conversion problems, placing the main focus on the interaction between time and uncertainty. This aspect is extremely relevant since most decisions made in the field of natural resources and sustainable development are irreversible decisions. In particular, we discuss and develop a scenario-based multi-stage stochastic programming model in order to determine the optimal land portfolio in time, given uncertainty affecting the market. The approach is then integrated in a decision tree framework in order to account for domain specific (environmental) uncertainty that, diversely from market uncertainty, may depend on the decision taken. Although, the designed methodology has many general applications, in the present work we focus on a particular case study, concerning a semi-degraded natural park located in northern Italy.

It is well known that motion detection using single frame differencing, while computationally much simpler than other techniques, is more liable to generate large areas of false foregrounds known as ghosts. In order to overcome this problem the authors propose a method based on signed differencing and connectivity analysis. The proposal is suitable to applications which cannot afford the un-avoidable errors of background modeling or the limitations of 3-frames preprocessing.

This paper focuses on on-line anomaly detection in video traffic surveillance systems. Markov chain (MC) have been proposed already in computer and network intrusion detection. We applied them to the traffic domain and we propose to extend the classical MC (modeling all the behaviors in the scene) with an approach that evaluates in parallel a set of behavior specific MC. Such separate MCs are more discriminatory than a single MC for all the behaviors, allowing our approach to detect anomalies resulting from joining segments of normal behaviors. The learning of such models is done by using sequences of labeled normal behaviors and discretizing the image plane by using a simple grid. The approach has been validated on traffic surveillance videos, and experimental results show good performance both in terms of precision and recall.

In this paper we are concerned with modelling techniques for evaluating development and conservation opportunities when dealing with investment decisions involving environmental resources management. In this context, highly characterized by both environmental and economic uncertainty, we emphasize the importance of capturing the flexibility of different investment strategies. In particular, we discuss a discrete model that includes both environmental and economic uncertainty treated using an integrated approach. Decision analysis techniques and option pricing theory are jointly applied to evaluate development versus conservation opportunities. In our analysis, we take into account how uncertainty interacts with two types of irreversibility: sunk costs associated with investment in developing decisions, including environmental and social costs, as well as sunk costs associated with environmental regulation and conservation. The Quasi Option Value, QOV, is used to derive decision rules that account for different levels of flexibility of land allocation possibilities.