Deep learning models for road passability detection during flood events using social media data

During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently

Deep Learning Models for Passability Detection of Flooded Roads

In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task

Automatic detection of passable roads after floods in remote sensed and social media data

This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches

Disaster Analysis using Satellite Image Data with Knowledge Transfer and Semi-Supervised Learning Techniques

With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative approaches to the mitigation and management of disaster operations efficiently [1]

The visual preferences for forest regeneration and field afforestation : four case studies in Finland

The overall aim of this dissertation was to study the public's preferences for forest regeneration fellings and field afforestations, as well as to find out the relations of these preferences to landscape management instructions, to ecological healthiness, and to the contemporary theories for predicting landscape preferences. This dissertation includes four case studies in Finland, each based on the visualization of management options and surveys. Guidelines for improving the visual quality of forest regeneration and field afforestation are given based on the case studies. The results show that forest regeneration can be connected to positive images and memories when the regeneration area is small and some time has passed since the felling. Preferences may not depend only on the management alternative itself but also on the viewing distance, viewing point, and the scene in which the management options are implemented. The current Finnish forest landscape management guidelines as well as the ecological healthiness of the studied options are to a large extent compatible with the public's preferences. However, there are some discrepancies. For example, the landscape management instructions as well as ecological hypotheses suggest that the retention trees need to be left in groups, whereas people usually prefer individually located retention trees to those trees in groups. Information and psycho-evolutionary theories provide some possible explanations for people's preferences for forest regeneration and field afforestation, but the results cannot be consistently explained by these theories. The preferences of the different stakeholder groups were very similar. However, the preference ratings of the groups that make their living from forest - forest owners and forest professionals - slightly differed from those of the others. These results provide support for the assumptions that preferences are largely consistent at least within one nation, but that knowledge and a reference group may also influence preferences.Väitöskirjassa tutkittiin ihmisten maisemapreferenssejä (maisemallisia arvostuksia) metsänuudistamishakkuiden ja pellonmetsitysten suhteen sekä analysoitiin näiden preferenssien yhteyksiä maisemanhoito-ohjeisiin, vaihtoehtojen ekologiseen terveyteen ja preferenssejä ennustaviin teorioihin. Väitöskirja sisältää neljä tapaustutkimusta, jotka perustuvat hoitovaihtoehtojen visualisointiin ja kyselytutkimuksiin. Tapaustutkimusten pohjalta annetaan ohjeita siitä, kuinka uudistushakkuiden ja pellonmetsitysten visuaalista laatua voidaan parantaa. Väitöskirjan tulokset osoittavat, että uudistamishakkuut voivat herättää myös myönteisiä mielikuvia ja muistoja, jos uudistusala on pieni ja hakkuun välittömät jäljet ovat jo peittyneet. Preferensseihin vaikuttaa hoitovaihtoehdon lisäksi mm. katseluetäisyys, katselupiste ja ympäristö, jossa vaihtoehto on toteutettu. Eri viiteryhmien (metsäammattilaiset, pääkaupunkiseudun asukkaat, ympäristönsuojelijat, tutkimusalueiden matkailijat, paikalliset asukkaat sekä metsänomistajat) maisemapreferenssit olivat hyvin samankaltaisia. Kuitenkin ne ryhmät, jotka saavat ainakin osan elannostaan metsästä - metsänomistajat ja metsäammattilaiset - pitivät metsänhakkuita esittävistä kuvista hieman enemmän kuin muut ryhmät. Nämä tulokset tukevat oletusta, että maisemapreferenssit ovat laajalti yhteneväisiä ainakin yhden kansan tai kulttuurin keskuudessa, vaikka myös viiteryhmä saattaa vaikuttaa preferensseihin jonkin verran. Nykyiset metsämaisemanhoito-ohjeet ovat pitkälti samankaltaisia tässä väitöskirjassa havaittujen maisemapreferenssien kanssa. Myöskään tutkittujen vaihtoehtoisten hoitotapojen ekologisen paremmuuden ja niihin kohdistuvien maisemallisten arvostusten välillä ei ollut suurta ristiriitaa. Kuitenkin joitakin eroavaisuuksia oli; esimerkiksi sekä maisemanhoito-ohjeiden että ekologisten hypoteesien mukaan säästöpuut tulisi jättää ryhmiin, kun taas ihmiset pitivät eniten yksittäin jätetyistä puista. Informaatiomalli ja psyko-evolutionaarinen teoria tarjoavat mahdollisia selityksiä uudistushakkuisiin ja pellonmetsitykseen kohdistuville preferensseille, vaikkakaan tutkimuksen tuloksia ei voida täysin selittää näillä teorioilla