Manual Mapping Research Articles

Automatic Fault Mapping in Remote Optical Images and Topographic Data With Deep Learning

AbstractFaults form dense, complex multi‐scale networks generally featuring a master fault and myriads of smaller‐scale faults and fractures off its trace, often referred to as damage. Quantification of the architecture of these complex networks is critical to understanding fault and earthquake mechanics. Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time‐consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U‐Net Convolutional Neural Network (CNN), to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on a number of performance tests, we select the best performing model, MRef, and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). MRefexhibits good generalization capacities, making it a viable tool for fast and accurate mapping of fracture and fault networks in image and topographic data. The MRefmodel can thus be used to analyze fault organization, geometry, and statistics at various scales, key information to understand fault and earthquake mechanics.

From survey to semantic representation for Cultural Heritage: the 3D modeling of recurring architectural elements

The digitization of Cultural Heritage paves the way for new approaches to surveying and restitution of historical sites. With a view to the management of integrated programs of documentation and conservation, the research is now focusing on the creation of information systems where to link the digital representation of a building to semantic knowledge. With reference to the emblematic case study of the Calci Charterhouse, also known as Pisa Charterhouse, this contribution illustrates an approach to be followed in the transition from 3D survey information, derived from laser scanner and photogrammetric techniques, to the creation of semantically enriched 3D models. The proposed approach is based on the recognition -segmentation and classification- of elements on the original raw point cloud, and on the manual mapping of NURBS elements on it. For this shape recognition process, reference to architectural treatises and vocabularies of classical architecture is a key step. The created building components are finally imported in a H-BIM environment, where they are enriched with semantic information related to historical knowledge, documentary sources and restoration activities.

An NLP-based citation reason analysis using CCRO

In recent scientific advances, Artificial Intelligence and Natural Language Processing are the major contributors to classifying documents and extracting information. Classifying citations in different classes have gathered a lot of attention due to the large volume of citations available in different digital libraries. Typical citation classification uses sentiment analysis, where various techniques are applied to citations texts to mainly classify them in “Positive”, “Negative” and “Neutral” sentiments. However, there can be innumerable reasons why an author selects another research for citation. Citations’ Context and Reasons Ontology—CCRO uses a clear scientific method to articulate eight basic reasons for citing by using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Using CCRO, this research paper adopts an ontology-based approach to extract citation's reasons and instantiate ontology classes and properties on two different corpora of citation sentences. One corpus of citation sentences is a publicly available dataset, while the other is our own manually curated. The process uses a two-step approach. The first part is an interface to manually annotate each citation text in the selected corpora on CCRO properties. A team of carefully selected annotators has annotated each citation to achieve a high inter-annotator agreement. The second part focuses on the automatic extraction of these reasons. Using Natural Language Processing, Mapping Graph, and Reporting Verb in a citation sentence, citation's reason is extracted and mapped onto a CCRO property. After comparing both manual and automatic mapping, accuracy is calculated. Based on experiments and results, accuracy is calculated for both publicly available and own corpora of citation sentences.

Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping

Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

Feasibility of manual bronchial branch reading technique in navigating conventional rEBUS bronchoscopy in the evaluation of peripheral pulmonary lesion.

Although radial endobronchial ultrasound (rEBUS) is an important verification tool in guided bronchoscopy, a navigational route was not provided. Manual airway mapping allows the bronchoscopist to translate the bronchial branching in computed tomography (CT) into a comparable bronchoscopic road map. We aimed to explore the feasibility of this technique in navigating conventional rEBUS bronchoscopy in the localisation of peripheral pulmonary lesion by determining navigation success and diagnostic yield. Retrospective review of consecutive rEBUS bronchoscopy performed with a 6.2 mm conventional bronchoscope navigated via manual bronchial branch reading technique over 18 months. Ninety-eight target lesions were included. Median lesion size was 2.67 cm (IQR 2.22-3.38) with 96.9% demonstrating positive CT bronchus sign. Majority (86.7%) of lesions were situated in between the third and fifth airway generations. Procedure was performed with endotracheal intubation in 43.9% and fluoroscopy in 72.4%. 98.9% of lesions were successfully navigated and verified by rEBUS following the pre-planned airway road map. Bidirectional guiding device was employed in 29.6% of cases. Clinical diagnosis was secured in 88.8% of cases, majority of which were malignant disease. The discrepancy between navigation success and diagnostic yield was 10.1%. Target PPL located within five airway generations was associated with better diagnostic yield (95.1% vs. 58.8%, P<0.001). There was 1 (1.0%) pneumothorax in our cohort. Manual bronchial branch reading technique in combination with conventional rEBUS is feasible in localisation of PPL, especially for lesions located within the first five airway generations.

Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand

The acquisition of landslide inventory data remains an important challenge for landslide susceptibility modelling. For rainfall-induced landslides, comprehensive mapping may be hindered by the size of storm-affected areas and large number of landslides generated as well as the time and costs involved in preparing multi-temporal inventories. In New Zealand, storm events trigger hundreds to thousands of shallow landslides, causing significant damage to land and infrastructure as well as impacts on freshwater and marine environments. Despite this, there are few quantitative assessments of shallow landslide susceptibility to inform targeting of control measures. Here, we compare the effect of using landslide inventories assembled from a) manual versus semi-automated mapping and b) event versus multi-temporal records on the performance of two widely applied methods for landslide susceptibility modelling, namely logistic regression and random forest classification. Evaluation of object-based image analysis (OBIA) for semi-automated landslide mapping showed mixed results, where producer's and user's accuracies ranged 62–81 and 45–55%, respectively, without manual refinement. However, the relative reduction of 6–11% in susceptibility model predictive performance based on area under receiver operating characteristic curves (AUC) using OBIA (AUC = 0.63–0.75) versus manual (AUC = 0.67–0.81) inventories with different variable combinations was low in comparison, and the spatial patterns in modelled susceptibility were generally similar. The random forest model produced slightly better prediction performance compared with logistic regression based on cross-validation within the same study area. However, this was reversed and logistic regression mostly outperformed random forest when the models were fitted and tested with data from different study areas. Model predictive performance for event versus multi-temporal records was comparable. Our results highlight both the challenges associated with semi-automated landslide detection over large areas as well as the opportunity to use OBIA for efficient data collection without necessarily compromising the resulting susceptibility maps. This potentially overcomes significant time and cost impediments to the preparation of landslide inventories that continue to hinder quantitative landslide susceptibility assessment.

Automated Detection of Marine Glacier Calving Fronts Using the 2-D Wavelet Transform Modulus Maxima Segmentation Method

Changes in the calving front position of marine-terminating glaciers strongly influence the mass balance of glaciers, ice caps, and ice sheets. At present, quantification of frontal position change primarily relies on time-consuming and subjective manual mapping techniques, limiting our ability to understand changes to glacier calving fronts. Here we describe a newly developed automated method of mapping glacier calving fronts in satellite imagery using observations from a representative sample of Greenland’s peripheral marine-terminating glaciers. Our method is adapted from the 2-D wavelet transform modulus maxima (WTMM) segmentation method, which has been used previously for image segmentation in biomedical and other applied science fields. The gradient-based method places edge detection lines along regions with the greatest intensity gradient in the image, such as the contrast between glacier ice and water or glacier ice and sea ice. The lines corresponding to the calving front are identified using thresholds for length, average gradient value, and orientation that minimize the misfit with respect to a manual validation data set. We demonstrate that the method is capable of mapping glacier calving fronts over a wide range of image conditions (light to intermediate cloud cover, dim or bright, mélange presence, etc.). With these time series, we are able to resolve subseasonal to multiyear temporal patterns as well as regional patterns in glacier frontal position change.

High-resolution semi-automatic mapping based on an Unmanned Aerial Vehicle (UAV) to capture geological structures.

Due to the recent technological progress, Unmanned Aerial Vehicles (UAV), is an alternative for the high-resolution imaging of the terrestrial surface, helping map lineaments, essential structures on the stage of geological mapping. Therefore, this research aims to accomplish and to confirm the efficiency of the use of UAV high-resolution imaging for semi-automatic lineament mapping, in a shear zone in Guaçuí/ES. Orthomosaic was created from the UAV imaging, in which the LINE algorithm was used from Geomath PCI software for the semi-automatic mapping of lineaments. In addition, a manual lineament mapping was performed on the orthomosaic for comparison purposes, and a shaded relief image was made up of Azimuth 135° and Elevation 45°, from the SRTM data, followed by manual lineament mapping for a regional vision of the studied area. On the semi-automatic and manual extraction on the orthomosaic and shaded relief image, 61.30%, 61.63%, 57.38% of the lineaments, respectively, have NW-SE direction, showing a really strong correlation. Therefore, the semi-automatic mapping is extremely effective in terms of structural trends acquisition and can provide fine-scale data for the assessment of inaccessible areas.

Supporting semantic PLM by using a lightweight engineering metadata mapping engine

In order to handle a high variety of interdisciplinary processes and complex smart products, integration platforms are a useful approach to view and access data all along the product lifecycle, which is stored in different data management solutions like Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP) and authoring systems. Here, the Metadata Repository for Semantic Product Lifecycle Management (SP²IDER) could serve as a supporting integration platform. An additional information layer on top of data source systems like PLM and ERP provides additional information, links data objects from different source systems, and provides access to these data. The SP²IDER platform consists of three basic parts: Connector units to fetch data from the source systems, a core unit with a Service Directory and a Mapping Engine, and a Metadata Store, where information about data objects is stored. This paper focuses on the view inside the Mapping Engine and the Metadata Store. The mapping engine has a three-way approach for the mapping of data objects and data types: The initial manual mapping at the data type level, second a rule-based, and third a machine-learning mapping at the data object level. This paper describes the manual mapping process, how the mapped data objects are stored inside the Metadata Store, and how this leads to a newly formed lightweight data model, that does not need heavyweight ontologies.

A Deep Network for Joint Registration and Parcellation of Cortical Surfaces.

Cortical surface registration and parcellation are two essential steps in neuroimaging analysis. Conventionally, they are performed independently as two tasks, ignoring the inherent connections of these two closely-related tasks. Essentially, both tasks rely on meaningful cortical feature representations, so they can be jointly optimized by learning shared useful cortical features. To this end, we propose a deep learning framework for joint cortical surface registration and parcellation. Specifically, our approach leverages the spherical topology of cortical surfaces and uses a spherical network as the shared encoder to first learn shared features for both tasks. Then we train two task-specific decoders for registration and parcellation, respectively. We further exploit the more explicit connection between them by incorporating the novel parcellation map similarity loss to enforce the boundary consistency of regions, thereby providing extra supervision for the registration task. Conversely, parcellation network training also benefits from the registration, which provides a large amount of augmented data by warping one surface with manual parcellation map to another surface, especially when only few manually-labeled surfaces are available. Experiments on a dataset with more than 600 cortical surfaces show that our approach achieves large improvements on both parcellation and registration accuracy (over separately trained networks) and enables training high-quality parcellation and registration models using much fewer labeled data.

ProGOMap: Automatic Generation of Mappings From Property Graphs to Ontologies

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap ( Pro perty G raph to O ntology M apper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

Comparison of Swarm Optimization and Memetic Algorithm for Systolic Mapping of Texture Analysis

Systolic processors offer a hardware design which can accommodate more functions in a small footprint. Hardware utilization efficiency can be enhanced by appropriately designating the intended hardware with a task in space and time through parallel computing platforms. Regular algorithms known for their computational complexity can be mapped to systolic array by dependence graphs, which allot hardware to the design data. Manual mapping techniques tend to be tedious with more inaccuracy and calls for efficient mapping techniques, automated through algorithmic procedures. Texture Analysis marks the preliminary progression of image analysis and interpretation. Automotive systems, Robotics, Industrial processing and similar automated applications can be simplified through texture analysis. This work deals with employing evolutionary algorithms for mapping texture analysis onto systolic architecture. Memetic Algorithms (MA) and Particle Swarm Optimization (PSO) algorithms were comparatively studied and the efficiency of designing a parallel architecture through systolic array is analyzed through cost function and processing time.

Cardiac transthyretin amyloidosis 99mTc-DPD SPECT correlates with strain echocardiography and biomarkers

PurposeHereditary transthyretin-amyloid amyloidosis (ATTRv) is an underdiagnosed condition commonly manifesting as congestive heart failure. Recently, scintigraphy utilizing DPD as a tracer was shown to identify ATTRv and wild-type ATTR cardiomyopathy. The aim of this study was to determine the value of quantified scintigraphy utilizing 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid (DPD) single-photon emission computed tomography (SPECT)/CT, and to correlate its uptake with well-established cardiac functional parameters.MethodsForty-eight patients with genetically verified ATTRv type-A fibril composition, positive 99mTc-DPD SPECT/CT, were retrospectively analyzed. Manual mapping of volumes of interest (VOIs) on DPD SPECT/CT examinations was used to quantify heart uptake. DPD mean and maximum uptake together with a calculated DPD-based amyloid burden (DPDload) was correlated with echocardiographic strain values and cardiac biomarkers.ResultsStatistically significant correlations were seen in VOIs between DPD uptakes and the corresponding echocardiographic strain values. Furthermore, DPDload had a strong correlation with echocardiographic strain parameters and also correlated with biomarkers troponin T and logarithmic NT-ProBNP.ConclusionsIn patients with ATTRv cardiomyopathy, DPD SPECT/CT measures the amyloid distribution and provides information on cardiac amyloid load. DPD amyloid load correlates with functional cardiac parameters.

DEFORESTATION MONITORING IN DIFFERENT BRAZILIAN BIOMES: CHALLENGES AND LESSONS

Abstract. Monitoring the conversion of native vegetation has challenged Brazilian government and scientists since the 1980s. In the case of the Amazonian forests, the Amazon Gross Deforestation Monitoring Project - PRODES has developed an effective methodology that provides consistent annual data on deforestation areas on a scale of 1:250,000, since 1988. In this article, we present some aspects of the evolution of this methodology, the key processes to produce accurate deforestation maps during the last 30 years and the new challenges that the Project would face. A central lesson is that no computational technique has, to date, been able to achieve the quality of deforestation maps produced by visual interpretation of satellite images and manual mapping.

Extracting and visualising glacial ice flow directions from Digital Elevation Models using greyscale thinning and directional trend analyses

Flow pattern reconstructions for past glaciations are based on the analysis of the spatial distribution of subglacial landforms, and streamlined subglacial landforms (oriented parallel or sub-parallel to the ice flow) are regarded as the main indicators of the previous ice flow. Manual mapping of these landforms is a time consuming and subjective process, making semi-automated mapping (SAM) methods attractive. We present a novel SAM method that extracts mean directional trends from a digital elevation model (DEM) by performing greyscale thinning on its derived slope raster, and produce directional statistics from the lines extracted from the resulting skeleton.The method has been tested on artificial (synthetically generated) surfaces to demonstrate its potential for detecting directions of idealized landforms. The tests carried out on the artificial surfaces reveal that a landform feature has to be at least six (raster) cells wide for its direction to be properly detected by the method. The application of the method on real-world terrain data shows that the method is capable of reconstructing glacial flow patterns, and that it is robust to reasonable variations in the method parameters. We believe that the method should also have great potential for detecting directional trends in the general field of geomorphology. The user friendliness of the method and the simple interpretation of the results should make this a useful tool for geomorphological studies.

A missing color area extraction approach from high-resolution statue images for cultural heritage documentation

Ancient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.

Students' Perception on the Use of Digital Mind Map to Stimulate Creativity and Critical Thinking in ESL Writing Course

In practise, mind maps have long been used by individuals especially teachers to create, visualise, arrange, and expand ideas in order to facilitate teaching and learning. Learning visually is a distinctive technique in modern pedagogy because most individuals are visual learners. As technology advances, digital mind map is created to replace manual mind map to bring modern pedagogy into the era of the fourth industrial revolution. Digital mind map has been used by teachers to teach diverse subjects. However, the use of digital mind map in teaching writing is scarce. Hence, the purpose of this case study was to examine the use of digital mind map to stimulate creativity and critical thinking in ESL writing course. A sample of 32 university students was selected randomly from one public university in Malaysia. The researchers developed a set of questionnaires to measure the three constructs – the usage of digital mind map, creativity, and critical thinking in writing tasks based on these three models: Lin & Faste, William, and Watson-Glaser models. The empirical results showed that the use of digital mind map evidently stimulates the students' creativity and critical thinking in completing their writing tasks as perceived by the respondents. Thus, the use of digital mind map has facilitated the ESL students' writing tasks in terms of integrating creative and critical elements.

A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data

The number of severe storm events has increased in recent decades due to climate change. These storms are one of the main causes for timber loss in European forests and damaged areas are prone to further degradation by, for example, bark beetle infestations. Usually, manual mapping of damaged areas based on aerial photographs is conducted by forest departments. This is very time-consuming and therefore automatic detection of windthrows based on active and passive remote sensing data is an ongoing research topic. In this study we evaluated state-of-the-art Convolutional Neural Networks (CNNs) in combination with Geographic Information Systems (GIS) for calamity assessment. The study area is in in the northern part of Hesse (Germany) and was covered by twelve Sentinel-2 scenes from 2018. Labels of damaged areas from the Friedericke storm (18 January 2018) were provided by HessenForst. We conducted several experiments based on a custom U-Net setup to derive the optimal architecture and input data as well as to assess the transferability of the model. Results highlight the possibility to detect damaged forest areas using Sentinel-2 data. Using a binary classification, accuracies of more than 92% were achieved with an Intersection over Union (IoU) score of 46.6%. The proposed workflow was integrated into ArcGIS and is suitable for fast detection of damaged areas directly after a storm and for disaster management but is limited by the deca-meter spatial resolution of the Sentinel-2 data.

Spatio-Temporal Dynamics of Algae and Macrophyte Cover in Urban Lakes: A Remote Sensing Analysis of Bellandur and Varthur Wetlands in Bengaluru, India

Rapid urbanization processes and indiscriminate disposal of urban wastewaters are major causes for anthropogenic lake-sediment deposition and eutrophication. However, information about the spatial and temporal variation of macrophyte and phytoplankton distribution as indicators for water contamination is limited. To gain insights into the dynamics, we analyzed lake-cover changes of Bellandur and Varthur Lake in the S-Indian megacity of Bengaluru for the post-rainy seasons of the years 2002–2019. Supervised maximum likelihood classifications were conducted on 62 freely available, true-color satellite images in order to distinguish between macrophytes, algae, and free water surface. The image-derived results were verified by supervised classification and manual mapping of two simultaneously recorded multispectral satellite images (Sentinel-2 and WorldView-2). Seasonal interrelations between macrophytes and algae distribution were similar for both lakes. The increase in macrophyte cover during post-rainy season negatively correlated with algal abundance. Macrophyte expansion progressively suppressed algae development at both lakes, reflective of increasing eutrophication caused by on-going wastewater input. Seasonal variation in precipitation, wind direction, and temperature seemed to trigger intra-annual shifts of macrophytes and algae while similar macrophyte spread intensities during the post-monsoon season indicated independence of nutrient loads in the lake water.

Green campus paradigms for sustainability attainment in higher education institutions – a comparative study

PurposeThis study aims to explore the current Green practices implemented in Malaysia higher education institutions toward sustainability attainment by developing a multi-disciplinary comprehensive policy framework to further extend the collaboration among sustainability practitioners in providing integrated data on Green indicators linked to economic, social and environmental dimensions of sustainability in higher education institutions.Design/methodology/approachPrimary data which comprises of sustainability archival documents from sixteen universities in Malaysia was used to explore the extent of Green practice for sustainability. In addition, this study also used secondary data from existing literature on sustainable development in Malaysia higher education institutional context.FindingsFindings from the examined 16 universities and prior sustainability studies in Malaysia universities suggest that higher education institutions in Malaysia are presently implementing Green practices in their university campuses toward attaining sustainability.Research limitations/implicationsQualitative data is only collected from higher education institutions in Malaysia. Hence, findings from this study cannot be generalized to universities in other countries.Practical implicationsThis study provides insights toward infusing Green campus paradigms from a technological perspective to facilitate the exchange of information between sustainability practitioners to produce innovative solutions for addressing sustainability challenges.Social implicationsThis research developed a policy framework that provides trans-disciplinary approach to be adopted by higher education institutions in Malaysia and further beyond toward attaining sustainability. Socially, this study provides Green indicators that act as a reference manual and road map toward sustainable development in higher education institutions.Originality/valueA novel multi-disciplinary comprehensive policy framework is developed grounded on identified Green indicators integrated to provide information on how sustainability practitioners can implement Green practices paradigms across universities. Furthermore, the Green indicators can be used as metrics to provide data for Green practice measurement and monitoring in higher education institutions.