Monitoring threatened irish habitats using multi-temporal multispectral aerial imagery and convolutional neural networks

The monitoring of threatened habitats is a key objective of European environmental policy. Due to the high cost of current field-based habitat mapping techniques there is a strong research interest in proposing solutions that reduce the cost of habitat monitoring through increasing their level of automation. Our work is motivated by the opportunities that recent advances in machine learning and Unmanned Aerial Vehicles (UAVs) offer to the habitat monitoring problem. In this paper, a deep learning based solution is proposed to classify four priority Irish habitats types present in the Maharees (Ireland) using UAV aerial imagery. The proposed method employs Convolutional Neural Networks (CNNs) to classify multi-temporal multi-spectral images of the study area corresponding to three different dates in 2020, obtaining an overall classification accuracy of 93%. A comparison of the proposed method with a multi-spectral 2D-CNN model demonstrates the advantage of including temporal information enabled by the proposed multi-temporal multi-spectral CNN model.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847402

Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery

The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy.This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Grant Agreement No. 847402. The authors would like to thank the EPA-funded iHabiMap project for providing the data used in this work. We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. The authors declare no conflicts of interes

A study of selection noise in collaborative web search

Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We consider this issue by analysing the results of collaborative Web search in circumstances where the behaviour of searchers is unreliable

Experiments in terabyte searching, genomic retrieval and novelty detection for TREC 2004

In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large scale, distributed information retrieval, which underlies all of the track experiments described in this document