Pedro Marco Achanccaray Diaz

Technische Universität Braunschweig · Department of Civil Engineering

This paper presents a new free software tool, named Segmentation Parameter Tuning 3 (SPT3), designed for automatic tuning of segmentation parameters based on a number of optimization algorithms using different quality metrics as fitness functions. For a segmentation algorithm to produce segments that correspond in some way to meaningful image objec...

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study in...

Accurate and up-to-date building and road data are crucial for informed spatial planning. In developing regions in particular, major challenges arise due to the limited availability of these data, primarily as a result of the inherent inefficiency of traditional field-based surveys and manual data generation methods. Importantly, this limitation ha...

Lightweight construction in structural engineering meets the principles of contemporary requirements for resource-efficient design and has already established itself as a building block for sustainable management in the construction industry of the high modernism. However, little is known about the planning, execution and sale of marketable lightwe...

Der globale Wandel stellt mit den einhergehenden außergewöhnlichen Wetterereignissen hohe Anforderungen an das Wassermanagement. In diesem Beitrag stellen wir das Forschungsprojekt EXDIMUM vor, das sich mit Fragestellungen zum Management der Auswirkungen von Extremwetter im Oberharz, insbesondere hinsichtlich Starkregen befasst. Im Projekt wird ein...

German seaports feature approximately 3,000 km of quay walls and 2,500 facilities. Many of these facilities are in poor conditions, according to the Federal Waterways Engineering and Research Institute. As a result, it has become an urgent necessity to inspect such port infrastructures. In this paper, we present an inspection framework that deals w...

Container cranes are of key importance for maritime cargo transportation. The uninterrupted and all-day operation of these container cranes, which directly affects the efficiency of the port, necessitates the continuous inspection of these massive hoisting steel structures. Due to the large size of cranes, the current manual inspections performed b...

During the High Modernism period spanning from approximately 1914 to 1970, the manufacturing of steel-constructed system halls witnessed a significant surge to accommodate the growing demand across various sectors such as industry, commerce, and agriculture. Surprisingly, these specific types of buildings have been largely overlooked in the realm o...

Im Jahr 2022 wurde in einem vorherigen Artikel dieser Zeitschriftenreihe das interdisziplinäre DFG Forschungsprojekt zum Systemhallenbau vorgestellt, das eine enge Kooperation zwischen dem Institut für Bauwerkserhaltung und Tragwerk sowie dem Institut für Geodäsie und Photogrammetrie der TU Braunschweig und dem Niedersächsischen Landesamt für Denkm...

Serial construction methods played an important role in the second half of the period of high modernism. This idea of serial production has been applied in various areas of everyday social and economic life. This research data publication deals with the listing of serially manufactured steel system halls and picks up the available attributes, which...

Serial Buildings and How to Find Them - An Artificial Intelligence Methodology for Building Detection. This chapter reports on the influence of the post-war years on system hall construction in the Federal Republic of Germany. The influence on the development of steel halls, as well as a small selection of the variety of special developments, are...

In the High Modernism period, from around 1914 to 1970, many system halls in steel construction were manufactured to meet the increasing demand in industry, commerce, and agriculture, among other areas. However, these types of buildings have not been the focus of any research in the field of construction history, generating a lack of knowledge rega...

La visión por computador es un área de estudio en la inteligencia artificial que se enfoca en el desarrollo de técnicas computacionales para percibir el mundo a través de entradas visuales, como videos o imágenes. El aprendizaje profundo ha demostrado ser una técnica eficiente para el análisis e interpretación de datos visuales. Sin embargo, afront...

Sea monitoring is essential for a better understanding of its dynamics and to measure the impact of human activities. In this context, remote sensing plays an important role by providing satellite imagery every day, even in critical climate conditions, for the detection of sea events with a potential risk to the environment. The present work propos...

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health pr...

In this work we propose a workflow to deal with overlaid images—images with superimposed text and company logos—, which is very common in underwater monitoring videos and surveillance camera footage. It is demonstrated that it is possible to use Explaining Artificial Intelligence to improve deep learning models performance for image classification...

The presence of weeds in agricultural crops has been one of the problems of greatest interest in recent years as they consume natural resources and negatively affect the agricultural process. For this purpose, a model has been implemented to segment weed in aerial images. The proposed model relies on DeepLabv3 architecture trained upon patches extr...

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health pr...

Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement...

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geograp...

In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error...

The earth population growth has continuously increased the demand for agricultural production. Thus, acreage and crop yield information become increasingly important. Techniques based on satellite images are one of the most attractive options for agricultural monitoring over large areas. Most of the scientific works on this application were develop...

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine le...

In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing, and crop rotation, makes agriculture highly dynamic. In this letter, we present the Campo Verde agricultural database. The purpose of creat...

Agriculture monitoring is a key task for producers, governments and decision makers. The analysis of multitemporal remote sensing data allows a cost-effective way to perform this task, mainly due to the increasing availability of free satellite imagery. Recurrent Neural Networks (RNNs) have been successfully used in temporal modeling problems, repr...

This work presents a comparative analysis of Deep Learning (DL) approaches for crop recognition from multitemporal sequences of SAR images. Convolutional Neural Networks (CNN) and Autoencoders (AE) are compared with a Random Forest (RF) classifier, all of them running on a feature space formed by image staking. Hand-crafted texture features were us...

Crop recognition from remote sensing images is a challenging task due to the dynamic behavior of different crops. The spectral appearance of a given crop changes over time because it is highly related to the phenological stage at each epoch or season, making it necessary to use sequences of images for a correct classification. Conditional Random Fi...

This work presents an approach for multispectral image classification that makes use of a Fuzzy Inference System (FIS). An IKONOS satellite sensor image of a neighborhood in Rio de Janeiro, Brazil has been used. The ground truth used in this work comprises six classes: trees, scrub, buildings, roads, water and shadows. Then, inputs sets, rules and...

Motivated by the rapidly increase of remote sensing data over the last years, this paper presents a tool designed for distributed image segmentation. InterSeg is able to handle efficiently very large high-resolution images using scalable distributed segmentation methods over computer clusters. Through a graphic user interface, InterSeg hides the di...

This letter evaluates metaheuristics for the supervised parameter tuning of multiresolution-region-growing segmentation. Three groups of metaheuristics are tested in terms of convergence speed and solution quality. Generalized pattern search, mesh adaptive direct search, and Nelder–Mead represent the single-solution group. Differential evolution (D...

This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at tw...

This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at tw...

Hyperspectral imaging is a technique in remote sensing that collect hundreds of images at differents wavelength values in the same area of the Earth. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 400 and 2500 nanometers. As a result, ea...

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images at differents wavelength values for the same area of the Earth. For instance the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 40 and 250 nanometers. As a result...

The Segmentation Parameter Tuner (SPT) is a tool designed for automatic tuning of segmentation parameters. In SPT, the goodness of a set of parameter values is given by the level of agreement between the segmentation result and a given reference (representing the desired outcome) quantified by a metric selected by the user (empirical discrepancy me...

This dissertation aims to evaluate segmentation algorithms for remote sensing images. Four segmentation algorithms were considered in this study. These algorithms have different approaches such as clustering-based, region growing-based, bayesian-based and graph-based. As each algorithm has its own parameters, the process to find their optimum value...