923 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Deep Learning Techniques for Geospatial Data Analysis
Consumer electronic devices such as mobile handsets, goods tagged with RFID
labels, location and position sensors are continuously generating a vast amount
of location enriched data called geospatial data. Conventionally such
geospatial data is used for military applications. In recent times, many useful
civilian applications have been designed and deployed around such geospatial
data. For example, a recommendation system to suggest restaurants or places of
attraction to a tourist visiting a particular locality. At the same time, civic
bodies are harnessing geospatial data generated through remote sensing devices
to provide better services to citizens such as traffic monitoring, pothole
identification, and weather reporting. Typically such applications are
leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes
Classifiers, Support Vector Machines, and decision trees. Recent advances in
the field of deep-learning showed that Neural Network-based techniques
outperform conventional techniques and provide effective solutions for many
geospatial data analysis tasks such as object recognition, image
classification, and scene understanding. The chapter presents a survey on the
current state of the applications of deep learning techniques for analyzing
geospatial data.
The chapter is organized as below: (i) A brief overview of deep learning
algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii)
Deep-learning techniques for Remote Sensing data analytics tasks (iv)
Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques
for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar,
Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning
Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning
Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher
Springer, Cham reproduced with permission of publisher Springer, Cha
Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data
Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively
Assessment of IBM and NASA's geospatial foundation model in flood inundation mapping
Vision foundation models are a new frontier in GeoAI research because of
their potential to enable powerful image analysis by learning and extracting
important image features from vast amounts of geospatial data. This paper
evaluates the performance of the first-of-its-kind geospatial foundation model,
IBM-NASA's Prithvi, to support a crucial geospatial analysis task: flood
inundation mapping. This model is compared with popular convolutional neural
network and vision transformer-based architectures in terms of mapping accuracy
for flooded areas. A benchmark dataset, Sen1Floods11, is used in the
experiments, and the models' predictability, generalizability, and
transferability are evaluated based on both a test dataset and a dataset that
is completely unseen by the model. Results show the impressive transferability
of the Prithvi model, highlighting its performance advantages in segmenting
flooded areas in previously unseen regions. The findings also suggest areas for
improvement for the Prithvi model in terms of adopting multi-scale
representation learning, developing more end-to-end pipelines for high-level
image analysis tasks, and offering more flexibility in terms of input data
bands.Comment: 11 pages, 4 figure
Trying to break new ground in aerial archaeology
Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models
Machine learning techniques are now integral to the advancement of
intelligent urban services, playing a crucial role in elevating the efficiency,
sustainability, and livability of urban environments. The recent emergence of
foundation models such as ChatGPT marks a revolutionary shift in the fields of
machine learning and artificial intelligence. Their unparalleled capabilities
in contextual understanding, problem solving, and adaptability across a wide
range of tasks suggest that integrating these models into urban domains could
have a transformative impact on the development of smart cities. Despite
growing interest in Urban Foundation Models~(UFMs), this burgeoning field faces
challenges such as a lack of clear definitions, systematic reviews, and
universalizable solutions. To this end, this paper first introduces the concept
of UFM and discusses the unique challenges involved in building them. We then
propose a data-centric taxonomy that categorizes current UFM-related works,
based on urban data modalities and types. Furthermore, to foster advancement in
this field, we present a promising framework aimed at the prospective
realization of UFMs, designed to overcome the identified challenges.
Additionally, we explore the application landscape of UFMs, detailing their
potential impact in various urban contexts. Relevant papers and open-source
resources have been collated and are continuously updated at
https://github.com/usail-hkust/Awesome-Urban-Foundation-Models
Assessing the changes in the moisture/dryness of water cavity surfaces in imlili sebkha in southwestern morocco by using machine learning classification in google earth engine
© 2020 by the authors. Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
- …