Automated Extraction of Labels from Large-Scale Historical Maps

Historical maps are frequently neither readable, searchable nor analyzable by machines due to lacking databases or ancillary information about their content. Identifying and annotating map labels is seen as a first step towards an automated legibility of those. This article investigates a universal and transferable methodology for the work with large-scale historical maps and their comparability to others while reducing manual intervention to a minimum. We present an end-to-end approach which increases the number of true positive identified labels by combining available text detection, recognition, and similarity measuring tools with own enhancements. The comparison of recognized historical with current street names produces a satisfactory accordance which can be used to assign their point-like representatives within a final rough georeferencing. The demonstrated workflow facilitates a spatial orientation within large-scale historical maps by enabling the establishment of relating databases. Assigning the identified labels to the geometries of related map features may contribute to machine-readable and analyzable historical maps.

Using Ancillary Information from Radar-based Observations and Rain Gauges to Identify Error and Bias

Ancillary information that exists within rain gauge and radar-based data sets provides opportunities to better identify error and bias between the two observing platforms as compared to error and bias statistics without ancillary information. These variables include precipitation type identification, air temperature, and radar quality. There are two NEXRAD based data sets used for reference; the National Centers for Environmental Prediction (NCEP) stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded data sets. The NCEP stage IV data set is available at 4km hourly and includes radar-gauge bias adjusted precipitation estimates. The NNR data set is available at 1km at 5-minute and hourly time intervals and includes several different variables such as reflectivity, radar-only estimates, precipitation flag, radar quality indicator, and radar-gauge bias adjusted precipitation estimates. The NNR data product provides additional information to apply quality control such as identification of precipitation type, identification of storm type and Z-R relation. Other measures of quality control are a part of the NNR data product development. In addition, some of the variables are available at 5-minute scale. We compare the radar-based estimates with the rain gauge observations from the U.S. Climate Reference Network (USCRN). The USCRN network is available at the 5-minute scale and includes observations of air temperature, wind, and soil moisture among others. We present statistical comparisons of rain gauge observations with radar-based estimates by segmenting information based on precipitation type, air temperature, and radar quality indicator.

Mapping parent material using data originating from Earth Observation as ancillary information

<p>Parent material is an essential soil property, whose mapping is a challenging task. Usually, large scale geological maps are used if they are available. However, in many cases, especially in medium and large scale mapping, such source data are too old or not existing at all. In this project have been looking for a solution for this problem. Our aim is to create a new, large scale, lithological map of parent material in an old mining region.</p><p>The study area is the Dorogi Basin in northern central Hungary. It is known for coal mining, which ended in 2003 after more than two centuries. The latest large scale (1:10,000) geological map series from this area was made in the 1960’s, in the “golden age” of mining.</p><p>Google Earth Engine was selected as main GIS platform, using mainly open source data and programs for mapping. We have used data originating from Earth Observation as ancillary information (e.g. satellite images, SRTM) and machine learning techniques to spatially predict parent material. The satellite images were used to calculate several geological indices, which can be used as indicators of chemical composition. We examined the use of multiple satellite platform (Sentinel-2, Landsat 8, ASTER) as it has different geological indices. The existing geological maps were used for training in the classification concerning the lithological composition.To predict the parent materials we have used random forest, using geomorphometric features and geological indices as predictors. The newly compiled map was validated by comparing it with the old one.</p><p><strong>Acknowledgment</strong>: Our research was supported by the Hungarian National Research,Development and Innovation Office (NKFIH; K-131820) and from the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.</p>

Application of information originating from spatially non-exhaustive ancillary observations in digital soil mapping

<p>Due to certain socio-economic processes and technical pressure, the number of potential data sources targeting the Earth’s surface increases rapidly as well as the data generated by them. Soil mapping heavily relied on these changes in the paradigm shift, which took place in the population and interpretation of spatial soil information in the last decade. In digital soil mapping practice, auxiliary, environmental co-variables, which are related to soil forming factors and processes, have been taken into account in spatially exhaustive form. However, the potential hidden in spatially non-exhaustive (most frequently point-like), ancillary information – originating from observations also targeting the soil mantle – is far from being exploited. In their thematic features, accuracy and reliability they are inferior to primary field and/or laboratory measurements collected directly, but they are generated in more facile, cheaper way, in greater volume, with denser temporal and spatial coverage and characteristically they are available in significantly easier form. Data sequences of various installed field sensors, data collections by proximal sensing techniques, information supply by farmers and land managers as well as citizen science are considered as possible information sources. Essentially, the (soft) data supplied by them don’t provide spatially exhaustive coverage, neither direct pedological reference, nevertheless they are hypothesized to be utilized as auxiliary information within DSM framework. In a recently started project we started to investigate, (i) in which way and with what efficiency these ancillary information originating from different secondary sources can be applied, furthermore (ii) in what manner their application influences (hopefully improves) the results, accuracy and reliability of goal-specific spatial predictions. The elaborated digital mapping procedures, which are based on (i) large amount of data with differing quality and (ii) integrated geostatistical and data mining methods can be absorbed in various earth and environmental science applications.</p><p> </p><p><strong>Acknowledgment:</strong> Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and Gábor Szatmári by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390).</p>

Examples of DAFNE application to multi-temporal and multi-frequency remote sensed images and geomorphic data for accurate flood mapping.

<p>DAFNE(Data Fusion by Bayesian Network) is a Matlab-based open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach [1]. It is based on Bayesian Networks and it is composed of five modules, which can be easily modified or upgraded to meet different user needs. DAFNE provides, as output products, probabilistic flood maps, i.e., for each pixel in a given output map, the probability value that the corresponding area has been reached from the inundation is reported. Moreover, if remote sensed images have been acquired in different days during a flood event, DAFNE allows to follow the inundation temporal evolution.</p><p>It is well known that flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground [2]. In particular, the combined analysis of multi-temporal and multi-frequency SAR intensity and coherence trends, together with optical data and other ancillary information, can be particularly useful to map flooded area, characterized by different land cover and land use [3]. Here a recent upgrade is presented that allows to consider as input data multi-frequency SAR intensity images, such as X-band, C-band and L-band images.</p><p>Three different inundation events have been considered as applicative examples: for each one, multi-temporal probabilistic flood maps have been produced by combining multi-temporal and multi-frequency SAR intensity images images (such as COSMO-SkyMed , Sentinel-1 images and ALOS 2 images), InSAR coherence and optical data (such as Landsat 5 images or High Resolution images), together with geomorphic and other ground information. Experimental results show good capabilities of producing accurate flood maps with computational times compatible with a near real time application.</p><p> </p><p>[1] A. D’Addabbo, A. Refice, F. Lovergine, G. Pasquariello, DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping. Computer and Geoscience 112 (2018), 64-75.</p><p>[2] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711–2722, 2014.</p><p>[3] A. D’Addabbo et al., “A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data,” IEEE Transactions on Geoscience and Remote Sensing, vol.54, n.6, pp.3612-3625, 2016.</p><p> </p>

Empirical Analysis of Ship Detection and Dimension Estimation from RISAT-1 Compact Polarimetric Data

The detection of ship and estimation of its characteristics has its significance in tracking these sea vessels for surveillance. It also has its importance as an ancillary information for marine applications like oil spill detection and its source tracking. This paper is an aspiration towards exploring the strength of RISAT-1 data retrieved in circular transmit linear receive mode. We propose a method to select the detected ship and get the dimension estimation of the segmented ship for further decision making. The proposed method has been implemented in python and tested for various inputs. The verification of the same is done as a de-facto measure based on location or by supporting measurements obtained through Google Maps.

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

Background In critical care settings, electroencephalography (EEG) with reduced number of electrodes (reduced montage EEG, rm-EEG) might be a timely alternative to the conventional full montage EEG (fm-EEG). However, past studies have reported variable accuracies for detecting seizures using rm-EEG. We hypothesized that the past studies did not distinguish between differences in sensitivity from differences in classification of EEG patterns by different readers. The goal of the present study was to revisit the diagnostic value of rm-EEG when confounding issues are accounted for. Methods We retrospectively collected 212 adult EEGs recorded at Massachusetts General Hospital and reviewed by two epileptologists with access to clinical, trending, and video information. In Phase I of the study, we re-configured the first 4 h of the EEGs in lateral circumferential montage with ten electrodes and asked new readers to interpret the EEGs without access to any other ancillary information. We compared their rating to the reading of hospital clinicians with access to ancillary information. In Phase II, we measured the accuracy of the same raters reading representative samples of the discordant EEGs in full and reduced configurations presented randomly by comparing their performance to majority consensus as the gold standard. Results Of the 95 EEGs without seizures in the selected fm-EEG, readers of rm-EEG identified 92 cases (97%) as having no seizure activity. Of 117 EEGs with “seizures” identified in the selected fm-EEG, none of the cases was labeled as normal on rm-EEG. Readers of rm-EEG reported pathological activity in 100% of cases, but labeled them as seizures (N = 77), rhythmic or periodic patterns (N = 24), epileptiform spikes (N = 7), or burst suppression (N = 6). When the same raters read representative epochs of the discordant EEG cases (N = 43) in both fm-EEG and rm-EEG configurations, we found high concordance (95%) and intra-rater agreement (93%) between fm-EEG and rm-EEG diagnoses. Conclusions Reduced EEG with ten electrodes in circumferential configuration preserves key features of the traditional EEG system. Discrepancies between rm-EEG and fm-EEG as reported in some of the past studies can be in part due to methodological factors such as choice of gold standard diagnosis, asymmetric access to ancillary clinical information, and inter-rater variability rather than detection failure of rm-EEG as a result of electrode reduction per se.