Roles of Geospatial Technologies in Hydrographic Practice

Any seafarer or mariner that uses the sea knows that navigation without correct charts is impossible and hazardous because nautical charts are the most essential and indispensable tools for vessels to sail safely at sea. For vessels to safely sail at sea, the seas and the oceans ought to be charted and this falls within the domain of hydrography. However, the seas cannot be charted effectively in the absence of the deployment of human resources and adequate tools like satellite and aerial imagery, survey boats and other equipment that will facilitate the hydrographic operations. The acquisition of data and information about the sea depths, nature of sea bed, waterways, navigational hazards and navigational objects among others, basically falls within the sphere of hydrography which is primarily known as survey at sea. The paper offers a review of geospatial technologies in hydrographic practice for enhanced safety of navigation at sea. The review is important to both the mariners, shipping industry and the government in order to explore the potentials provided by Geographic Information System, Remote Sensing, cloud GIS, big data GIS and Global Positioning System to enhance the practice of hydrography. The data and materials used for the review were obtained from literature in the internet and other published works. The paper looked at hydrography as a profession, roles of geospatial technologies in hydrographic practice, benefits of hydrography to national development and finally, the weaknesses of geospatial technologies in hydrographic practice were equally examined.

Aerial Imagery Paddy Seedlings Inspection Using Deep Learning

In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning

PurposeDemolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery.MethodsOutcomes were annual counts of fatal and non-fatal shootings in Rochester, New York, from 2000 to 2020. Treatment was demolitions conducted from 2009 to 2019. Units of analysis were 152×152 m grid squares. We used a difference-in-differences approach to test effects: (A) the year after each demolition and (B) as demolitions accumulated over time. As moderators, we used a built environment typology generated by extracting information from aerial imagery using convolutional neural networks, a deep learning approach, combined with k-means clustering. We stratified our main models by built environment cluster to test for moderation.ResultsOne demolition was associated with a 14% shootings reduction (incident rate ratio (IRR)=0.86, 95% CI 0.83 to 0.90, p<0.001) the following year. Demolitions were also associated with a long-term, 2% reduction in shootings per year for each cumulative demolition (IRR=0.98, 95% CI 0.95 to 1.00, p=0.02). In the stratified models, densely built areas with higher street connectivity displayed following-year effects, but not long-term effects. Areas with lower density and larger parcels displayed long-term effects but not following-year effects.ConclusionsThe built environment might influence the magnitude and duration of the effects of demolitions on firearm violence. Policymakers may consider complementary programmes to help sustain these effects in high-density areas.

Counting using deep learning regression gives value to ecological surveys

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, which opens opportunities to automatise counting tasks. In this study, we use machine learning to automate counting objects of interest without the need to label individual objects. By leveraging already existing image-level annotations, this approach can also give value to historical data that were collected and annotated over longer time series (typical for many ecological studies), without the aim of deep learning applications. We demonstrate deep learning regression on two fundamentally different counting tasks: (i) daily growth rings from microscopic images of fish otolith (i.e., hearing stone) and (ii) hauled out seals from highly variable aerial imagery. In the otolith images, our deep learning-based regressor yields an RMSE of 3.40 day-rings and an $$R^2$$ R 2 of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and $$R^2$$ R 2 of 0.72), which can be attributed to a lack of images with a high number of seals in the initial training set, compared to the test set. We then show how to improve performance substantially (RMSE of 19.03 seals and $$R^2$$ R 2 of 0.77) by carefully selecting and relabelling just 100 additional training images based on initial model prediction discrepancy. The regression-based approach used here returns accurate counts ($$R^2$$ R 2 of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research.

Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network

The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed human–computer interaction, which was a daunting proposition. To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. The proposed CNN has three parallel stream channels: the first is the high-resolution aerial imagery, while the second stream is the digital surface model (DSM). The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. Furthermore, the channel has eight group convolution blocks of 2D convolution with three max-pooling layers. The proposed model’s efficiency and dependability were tested on three different categories of complex urban building structures in the study area. Then, morphological operations were applied to the extracted building footprints to increase the uniformity of the building boundaries and produce improved building perimeters. Thus, our approach bridges a significant gap in detecting building objects in diverse environments; the overall accuracy (OA) and kappa coefficient of the proposed method are greater than 80% and 0.605, respectively. The findings support the proposed framework and methodologies’ efficacy and effectiveness at extracting buildings from complex environments.

The Evolution of the Popocatépetl Volcanic Complex: Constraints on Periodic Edifice Construction and Destruction by Sector Collapse

Popocatépetl is one of the most active volcanoes in North America. Its current predominantly mild activity is contrasted by a history of large effusive and explosive eruptions and sector collapse events, which was first summarised by Espinasa-Pereña and Martin-Del Pozzo (2006). Since then, a wealth of new radiometric, geophysical and volcanological data has been published, requiring a re-evaluation of the evolution of the Popocatépetl Volcanic Complex (PVC). Herein, we combine existing literature with new field observations, aerial imagery and digital elevation model interpretations to produce an updated and improved reconstruction of the growth and evolution of the PVC through all of its history. This will be fundamental for the assessment and mitigation of risks associated with potential future high-magnitude activity of the PVC. The PVC consists of four successive volcanic edifices separated by three sector collapse events producing avalanche deposits: Tlamacas (>538 - >330 ka, described here for the first time), Nexpayantla (∼330 - >96 ka), Ventorrillo (∼96 ka - 23.5 ka) and Popocatépetl (<23.5ka) edifices. The newly described Tlamacas collapse propagated towards ENE forming part of the Mayorazgo avalanche deposit.Supplementary material:https://doi.org/10.6084/m9.figshare.c.5709190