A low cost mobile mapping system (LCMMS) for field data acquisition: a potential use to validate aerial/satellite building damage assessment

Among the major natural disasters that occurred in 2010, the Haiti earthquake was a real turning point concerning the availability, dissemination and licensing of a huge quantity of geospatial data. In a few days several map products based on the analysis of remotely sensed data-sets were delivered to users. This demonstrated the need for reliable methods to validate the increasing variety of open source data and remote sensing-derived products for crisis management, with the aim to correctly spatially reference and interconnect these data with other global digital archives. As far as building damage assessment is concerned, the need for accurate field data to overcome the limitations of both vertical and oblique view satellite and aerial images was evident. To cope with the aforementioned need, a newly developed Low-Cost Mobile Mapping System (LCMMS) was deployed in Port-au-Prince (Haiti) and tested during a five-day survey in FebruaryMarch 2010. The system allows for acquisition of movies and single georeferenced frames by means of a transportable device easily installable (or adaptable) to every type of vehicle. It is composed of four webcams with a total field of view of about 180 degrees and one Global Positioning System (GPS) receiver, with the main aim to rapidly cover large areas for effective usage in emergency situations. The main technical features of the LCMMS, the operational use in the field (and related issues) and a potential approach to be adopted for the validation of satellite/aerial building damage assessments are thoroughly described in the articl

Vehicle trajectory prediction and generation using LSTM models and GANs

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services

Rapid Mapping: geomatics role and research opportunities

In recent years an increasing number of extreme meteorological events have been recorded. Geomatics techniques have been historically adopted to support the different phases of the Emergency Management cycle with a main focus on emergency response, initial recovery and preparedness through the acquisition, processing, management and dissemination of geospatial data. In the meantime, the increased availability of geospatial data in terms of reference topographic datasets, made available by authoritative National Mapping Cadastre Agencies or by Collaborative Mapping initiatives like OpenStreetMap, as well as of remotely sensed imagery, poses new challenges to the Geomatics role in defining operational tools and services in support of emergency management activities. This paper is mainly focused on the role of Geomatics in supporting the response phase of the Emergency Management cycle through Rapid Mapping activities, which can be defined as “the on-demand and fast provision (within hours or days) of geospatial information in support of emergency management activities immediately following an emergency event” (source: European Union, http://emergency.copernicus.eu/mapping/ems/service-overview). Management of geospatial datasets (both reference and thematic), Remote Sensing sensors and techniques and spatial information science methodologies applied to Rapid Mapping will be described, with the goal to highlight the role that Geomatics is currently playing in this domain. The major technical requirements, constraints and research opportunities of a Rapid Mapping service will be discussed, with a specific focus on: the time constraints of the service, the data quality requirements, the need to provide replicable products, the need for consistent data models, the advantages of data interoperability, the automation of feature extraction procedures to reduce the need for Computer Aided Photo Interpretation, the dissemination strategies

Mappatura speditiva dei danni da immagini satellitari a supporto della risposta all’emergenza

This essay describes rapid mapping activities based on satellite imagery and it is focused on earthquake damage assessment carried out by the Rapid Mapping component of the Copernicus Emergency Management Service (CEMS). In the introduction section the potentials of satellite sensors, especially the very-high resolution optical ones, in providing value-added information to support the emergency response phase are discussed. Operational satellite-based emergency mapping mechanisms and their operational adoption at global level is also described. The second section provides a quick description of the European Union’s Earth Observation Copernicus programme and its six different services, focusing on the Rapid Mapping service. In the third section the chronological history of the CEMS activations in response to the 2 main earthquake events that hit Central Italy in 2016 and 2017 is described, proving the details of the type of analyses and of products generated by the service. Finally, the fourth and last section contains a critical review of the activities carried out and of the related mapping products, highlighting benefits and drawbacks of the current approach: a possible workflow to integrate field surveys aimed at complementing damage assessment based on satellite imagery only is also suggested and discussed

EARLY IMPACT PROCEDURES FOR FLOOD EVENTSFEBRUARY 2007 MOZAMBIQUE FLOOD

Satellite images and GIS procedures are key elements for emergency management, especially in case of events hitting developing countries, more vulnerable to calamities and less prepared to face them. This article aims to show the procedure applied for the pro-duction of a cartography of flooded areas during the early impact phase; these activities are developed within ITHACA, centre of excellence, in charge of giving technological support to the WFP (World Food Programme), the biggest agency of the UN. The flood in Mozambique, occurred in January 2007, is illustrated as an example of events management

Multi-platform, Multi-scale and Multi-temporal 4D Glacier Monitoring. The Rutor Glacier Case Study

At present most alpine glaciers are not in equilibrium with the current climate, as a result they are undergoing a dramatic mass loss. Monitoring glacial variations is crucial to assess the consequences of climate change on the territory. In this work different geomatics techniques are exploited to measure and monitor the Rutor glacier over the years. In this study two different techniques were adopted to generate 3 digital surface models (DSMs): aerial and satellite photogrammetry. Two photogrammetric aerial surveys were carried out: at the end of the hydrological year 2019/20 and at the end of the following hydrological year. Additionally, a very high-resolution satellite stereo pair, acquired by the Pléiades-1A platform in 2017, was processed to assess whether satellite images can be applied to extract the 3D surface of the Rutor glacier. In order to evaluate the Rutor glacier mass-balance throughout the years several reference points were positioned and measured before the 2021 aerial flight. Thanks to the presence of the materialized points the 2021 model is considered as the ‘Reference Model’ against which subsequent models can be compared for glacier analysis. This model was validated by means of a comparison with the authoritative Regional DSM based on LiDAR surveys. In alpine glaciers, the positioning of artificial square cross target in time invariant areas is crucial to enable a multitemporal 4D analysis. The use of very high-resolution satellite imagery allows large areas to be mapped in 3D, but with lower accuracies proportionally decreasing with respect to slope and exposure

D1.5 ‐ Data Management Plan

The Working Package 1 'Project Management and Coordination", Task 1.3 "Data Management Plan (DMP), open science practices and Research Data Management" of the GreenFORCE project comprises the Deliverable 1.5 – Data Management Plan (DMP). This document is the first Version of the DMP, which, as per the Grant Agreement, is to be submitted within the 6th month of the GreenFORCE Project, more precisely, within 31/12/2022. In case needed, an updated second version of the DMP shall be shared in the 18th month of the project duration. The DMP produced in the framework of the GreenFORCE Project is a living document that describes how to research information (research documents and data) will be managed during and after the project duration (the data life cycle). The document is prepared based on the Horizon DMP Template1 and reflects the Grant Agreement and Consortium Agreement requirements. Furthermore, the DMP describes the data that will be generated/created/used, how the generated/created/used data will be shared and preserved in the short and long term, and what restrictions (if any) will be applied. The approach for the DMP preparation evolved in steps. Preliminarily, all partners in the GreenFORCE Project provided information regarding data management policy and practices in respective institutions through a structured questionnaire. Part of the information provided has been embedded in the current version of the DMP. In a second step, the draft DMP was shared with all partners, providing information regarding research documents and data used/produced in the framework of the GreenFORCE Project. Once all comments were addressed, the DMP was submitted to the Ethics Advisor for clearance. Finally, the DMP was finalised and uploaded into the system on 31/12/2022. In its first Version (V1), the DMP provides the general approach and policy to the data management of the GreenFORCE Project. It is expected that during the GreenFORCE Project implementation, the DMP will be updated regularly and accordingly, gaining precision and substance