Product Management and Road Mapping Tool Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 6.35 Billion by 2031, growing at a CAGR of 6.55% from 2024 to 2031.

Global Product Management and Road Mapping Tool Market Drivers

Government and Private Sector Investments: Government initiatives and private sector investments in healthcare infrastructure are contributing significantly to the market's growth. Programs aimed at improving eye care services, particularly in developing regions, are driving the adoption of high-quality ophthalmic equipment, including instrument tables. Additionally, funding for research and development in ophthalmology supports the market's expansion.

Rising Awareness and Routine Eye Examinations: Increased public awareness about the importance of regular eye check-ups has led to a rise in the frequency of ophthalmic consultations. Routine eye examinations require reliable and functional instrument tables to ensure accurate diagnostics and effective treatments, further fueling market demand.

View what City-and-state planned roadway project(s) may be taking place near a specific property.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

Decarbonization Road-Mapping Software Market Outlook

According to our latest research, the global decarbonization road-mapping software market size reached USD 1.25 billion in 2024, reflecting the rapid adoption of digital solutions for sustainability planning across industries. The market is expected to grow at a CAGR of 14.8% during the forecast period, with the value projected to reach USD 3.85 billion by 2033. This substantial growth is primarily driven by heightened regulatory pressures, increasing corporate commitments to net-zero targets, and the growing complexity of carbon management across global supply chains. As organizations strive to meet ambitious sustainability goals, the demand for robust decarbonization road-mapping software has surged, making it a critical tool in the transition to a low-carbon economy.

One of the primary growth factors for the decarbonization road-mapping software market is the intensifying regulatory landscape. Governments worldwide are implementing stricter emissions reduction targets and mandating transparent reporting on carbon footprints. This regulatory push is compelling organizations across sectors to adopt advanced digital tools for accurate emissions tracking, scenario analysis, and strategic planning. Decarbonization road-mapping software enables businesses to navigate complex compliance requirements, simulate the impact of various decarbonization strategies, and streamline the integration of sustainability into core business operations. As regulatory frameworks continue to evolve, the need for agile and scalable software solutions becomes even more pronounced, further fueling market expansion.

Another significant driver is the growing emphasis on corporate sustainability and ESG (Environmental, Social, and Governance) reporting. Investors, customers, and stakeholders are increasingly demanding transparency regarding organizations’ climate action plans and progress toward net-zero commitments. Decarbonization road-mapping software empowers companies to set science-based targets, monitor emissions in real-time, and generate comprehensive reports aligned with global standards such as the GHG Protocol and TCFD (Task Force on Climate-related Financial Disclosures). By leveraging these platforms, organizations not only enhance their sustainability credentials but also mitigate risks associated with greenwashing and regulatory non-compliance. The integration of AI and machine learning within these solutions further enhances predictive analytics, enabling proactive decision-making and optimized resource allocation.

Technological advancements and the proliferation of cloud-based solutions are also accelerating market growth. Cloud deployment models offer scalability, flexibility, and seamless integration with existing enterprise systems, making decarbonization road-mapping software accessible to organizations of all sizes. The rise of Industry 4.0 and digital transformation initiatives has paved the way for the adoption of sophisticated analytics, IoT-enabled data collection, and real-time visualization tools. These innovations facilitate granular emissions tracking, dynamic scenario modeling, and automated reporting, thereby increasing operational efficiency and reducing the total cost of ownership. As digital maturity increases across industries, the adoption curve for decarbonization software is expected to steepen, driving robust growth through 2033.

From a regional perspective, Europe currently dominates the decarbonization road-mapping software market, accounting for the largest share in 2024, followed closely by North America. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, urbanization, and government-led decarbonization initiatives. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions ramp up their climate action efforts and invest in digital sustainability solutions. The global nature of supply chains and cross-border regulatory requirements further underscore the importance of comprehensive, interoperable software platforms, positioning the market for sustained expansion across all major regions.

Component Analysis

The decarbonization road-mapping software market is segmented by component into software and services, each playing a pivotal role in delivering end

Product Management and Road Mapping Tool comes with extensive industry analysis of development components, patterns, flows, and sizes. The report calculates present and past market values to forecast potential market management during the forecast period between 2024 - 2032.

High-density linkage maps are important tools for genome biology and evolutionary genetics by quantifying the extent of recombination, linkage disequilibrium and chromosomal rearrangements across chromosomes, sexes and populations. They provide one of the best ways to validate and refine de novo genome assemblies, with the power to identify errors in assemblies increasing with marker density. However, assembly of high-density linkage maps is still challenging due to software limitations. We describe Lep-MAP2, a software for ultra-dense genome-wide linkage map construction. Lep-MAP2 can handle various family structures and can account for achiasmatic meiosis to gain linkage map accuracy. Simulations show that Lep-MAP2 outperforms other available mapping software both in computational efficiency and accuracy. When applied to two large F2-generation recombinant crosses between two nine-spined stickleback (Pungitius pungitius) populations, it produced two high-density (~6 markers/cM) linkag...

800+ GIS Engineers with 25+ years of experience in geospatial, We provide following as Advance Geospatial Services:

Analytics (AI) Change detection Feature extraction Road assets inventory Utility assets inventory Map data production Geodatabase generation Map data Processing /Classifications
Contour Map Generation Analytics (AI) Change Detection Feature Extraction Imagery Data Processing Ortho mosaic Ortho rectification Digital Ortho Mapping Ortho photo Generation Analytics (Geo AI) Change Detection Map Production Web application development Software testing Data migration Platform development

AI-Assisted Data Mapping Pipeline AI models trained on millions of images are used to predict traffic signs, road markings , lanes for better and faster data processing

Our Value Differentiator

Experience & Expertise -More than Two decade in Map making business with 800+ GIS expertise -Building world class products with our expertise service division & skilled project management -International Brand “Mappls” in California USA, focused on “Advance -Geospatial Services & Autonomous drive Solutions”

Value Added Services -Production environment with continuous improvement culture -Key metrics driven production processes to align customer’s goals and deliverables -Transparency & visibility to all stakeholder -Technology adaptation by culture

Flexibility -Customer driven resource management processes -Flexible resource management processes to ramp-up & ramp-down within short span of time -Robust training processes to address scope and specification changes -Priority driven project execution and management -Flexible IT environment inline with critical requirements of projects

Quality First -Delivering high quality & cost effective services -Business continuity process in place to address situation like Covid-19/ natural disasters -Secure & certified infrastructure with highly skilled resources and management -Dedicated SME team to ensure project quality, specification & deliverables

This layer is displayed on the Zone map in the City Plan version 7 as 'Functional road hierarchy', and all index maps as 'Major road network'. The layer is also available in Council’s City Plan interactive mapping tool. For further information on City Plan, please visit http://www.goldcoast.qld.gov.au/planning-and-building/city-plan-2015-19859.html

Single nucleotide polymorphism (SNP) markers and high-density genetic maps are important resources for marker-assisted selection, mapping of quantitative trait loci (QTLs), and genome structure analysis. Although linkage maps in certain catfish species have been obtained, high-density maps remain unavailable in the economically important southern catfish (Silurus meridionalis). Recently developed restriction site-associated DNA (RAD) markers have proven to be a promising tool for SNP detection and genetic map construction. The objective of this study was to construct a high-density linkage map using SNPs generated by next-generation RAD sequencing in S. meridionalis for future genetic and genomic studies. An F1 population of 100 individuals was obtained by intraspecific crossing of two wild heterozygous individuals. In total, 77,634 putative high-quality biallelic SNPs between the parents were discovered by mapping the parents’ paired-end RAD reads onto the reference contigs from both parents, of which 54.7% were transitions and 45.3% were transversions (transition/transversion ratio of 1.2). Finally, 26,714 high-quality RAD markers were grouped into 29 linkage groups by using de novo clustering methods (Stacks). Among these markers, 4,514 were linked to the female genetic map, 23,718 to the male map, and 6,715 effective loci were linked to the integrated map spanning 5,918.31 centimorgans (cM), with an average marker interval of 0.89 cM. High-resolution genetic maps are a useful tool for both marker-assisted breeding and various genome investigations in catfish, such as sequence assembly, gene localisation, QTL detection, and genome structure comparison. Hence, such a high-density linkage map will serve as a valuable resource for comparative genomics and fine-scale QTL mapping in catfish species.

The report on Strategy And Innovation Road Mapping Tools covers a summarized study of several factors supporting market growth, such as market size, market type, major regions, and end-user applications. The report enables customers to recognize key drivers that influence and govern the market.

Strategy and Innovation Road mapping Tools Market size was valued at USD 432.85 Billion in 2024 and is projected to reach USD 1203.37 Billion by 2032, growing at a CAGR of 15.04% during the forecast period 2026-2032.

Global Strategy and Innovation Road mapping Tools Market Drivers

Need for Strategic Planning: Organizations across various industries increasingly recognize the importance of strategic planning to navigate market uncertainties and competitive pressures. Roadmapping tools help businesses align their long-term vision with actionable steps, ensuring coherent strategy execution.

Accelerating Pace of Technological Change: Rapid advancements in technology necessitate continuous innovation. Roadmapping tools assist companies in tracking emerging technologies, assessing their impact, and integrating them into their strategic plans, thereby maintaining competitiveness.

Increased Focus on Innovation Management: To stay ahead in the market, companies are prioritizing innovation management. Roadmapping tools provide a structured approach to manage innovation processes, from ideation to implementation, ensuring that innovative ideas are systematically developed and commercialized.

Complexity of Product Development: Modern product development involves multiple stages and stakeholders. Roadmapping tools streamline this complexity by providing visual representations of timelines, milestones, and dependencies, facilitating better coordination and collaboration across teams.

Demand for Agile and Flexible Planning: The volatile business environment demands agility. Roadmapping tools enable organizations to adapt their strategies dynamically in response to market changes, regulatory shifts, and new opportunities, supporting an agile approach to planning and execution.

Integration with Digital Transformation Initiatives: As businesses embark on digital transformation journeys, roadmapping tools play a critical role in aligning digital initiatives with overall business strategy. These tools help map out digital projects, ensuring they contribute to the broader organizational goals.

Enhanced Collaboration and Communication: Effective collaboration and communication are essential for successful strategy execution. Roadmapping tools provide a centralized platform where teams can collaborate, share insights, and keep track of progress, fostering a collaborative environment.

Regulatory and Compliance Requirements: In industries with stringent regulatory requirements, roadmapping tools help organizations ensure compliance by integrating regulatory milestones and timelines into their strategic plans, reducing the risk of non-compliance.

Competitive Pressure and Market Dynamics: The need to stay competitive in a rapidly changing market drives the adoption of roadmapping tools. These tools help organizations anticipate market trends, identify competitive threats, and develop strategies to capitalize on opportunities.

Adoption of Data-Driven Decision Making: The increasing reliance on data-driven decision-making necessitates tools that can integrate various data sources and provide actionable insights. Roadmapping tools leverage data analytics to inform strategic decisions, enhancing the accuracy and effectiveness of strategic planning.

Drone-Based Road Surface Albedo Mapping Market Outlook

According to our latest research, the global drone-based road surface albedo mapping market size reached USD 642.3 million in 2024, supported by the increasing demand for advanced road surface monitoring and data-driven infrastructure management. The market is experiencing robust growth, registering a CAGR of 13.6% from 2025 to 2033. By 2033, the market is projected to attain a value of USD 1,987.6 million, reflecting the rapid adoption of drone technologies and sophisticated mapping solutions across multiple sectors. The primary growth driver is the surging need for precise, real-time surface albedo data to enhance road safety, optimize maintenance cycles, and support environmental sustainability initiatives.

The growth trajectory of the drone-based road surface albedo mapping market is primarily influenced by the integration of advanced sensor technologies, such as multispectral and thermal imaging, which enable high-resolution and accurate mapping of road surfaces. Governments and private stakeholders are increasingly recognizing the value of albedo data in mitigating urban heat island effects, improving energy efficiency in urban planning, and enhancing road surface durability. As urbanization accelerates globally, the pressure to maintain and upgrade transportation infrastructure has intensified, further propelling market expansion. The ability of drone-based systems to cover large areas efficiently and provide actionable insights is transforming traditional road monitoring practices, making them indispensable tools for modern infrastructure management.

Another significant factor fueling market growth is the increasing emphasis on environmental monitoring and sustainability. With climate change and extreme weather events becoming more frequent, there is a heightened focus on understanding the thermal properties of road surfaces and their impact on local microclimates. Drone-based albedo mapping offers a non-invasive, cost-effective solution for collecting comprehensive environmental data, which is crucial for designing resilient infrastructure and complying with stringent environmental regulations. The proliferation of smart city initiatives and the integration of Internet of Things (IoT) technologies are further amplifying the demand for real-time, high-fidelity road surface data, creating new opportunities for market players.

Technological advancements in drone hardware, software analytics, and data processing capabilities are also contributing to the robust growth of the market. The evolution of artificial intelligence (AI) and machine learning algorithms has enabled the automation of data interpretation, reducing human error and increasing the speed at which actionable insights can be derived. These advancements are making drone-based road surface albedo mapping more accessible to a wide range of end-users, from government agencies to private construction firms. Furthermore, falling hardware costs and the emergence of cloud-based data management platforms are lowering entry barriers, allowing smaller organizations to leverage the benefits of drone-based mapping solutions.

From a regional perspective, North America currently dominates the market, accounting for a significant share due to its advanced infrastructure, high adoption of innovative technologies, and supportive regulatory environment. However, the Asia Pacific region is expected to witness the fastest growth, driven by rapid urbanization, large-scale infrastructure development, and increasing government investments in smart cities and sustainable transportation. Europe also represents a substantial market, with strong emphasis on environmental sustainability and the modernization of transportation networks. Latin America and the Middle East & Africa are emerging markets, gradually adopting drone-based mapping solutions as part of broader infrastructure modernization efforts.

Component Analysis

The component segment of the drone-based road surface albedo mapping market is divided into hardware, software, and services, each playing a crucial role in the overall value chain. Hardware forms the backbone of this market, encompassing drones, sensors, cameras, and related equipment required for data acquisition. The rapid evolution of drone technology has led to the development of more robust, lightweight, and energy-efficient platforms capable of carrying advanced multispectral and the

Drone-Based Road Surface Albedo Mapping Market Outlook

According to our latest research, the drone-based road surface albedo mapping market size reached USD 435 million in 2024. The market is experiencing robust growth, supported by a compound annual growth rate (CAGR) of 17.6% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 1.62 billion. This rapid expansion is driven by increasing investments in smart city initiatives, the urgent need for efficient road maintenance, and growing awareness regarding environmental sustainability. As per our latest research, the integration of advanced drone technologies with sophisticated mapping software is expected to further accelerate market adoption worldwide.

A primary growth factor for the drone-based road surface albedo mapping market is the escalating demand for precise and real-time data for urban planning and infrastructure management. Traditional road surface evaluation methods are often labor-intensive, time-consuming, and prone to human error. In contrast, drone-based mapping offers high-resolution data collection, enabling authorities and stakeholders to make data-driven decisions regarding road maintenance, resurfacing, and urban layout. The ability to monitor albedo, or the reflectivity of road surfaces, allows for more effective management of urban heat islands and supports climate adaptation strategies. This technological advancement is particularly relevant as cities expand and climate change mitigation becomes a strategic priority for governments and urban planners globally.

Another significant driver is the evolution of drone hardware and software, which has substantially enhanced the efficiency and accuracy of road surface albedo mapping. Modern drones are equipped with multispectral and thermal sensors, LiDAR, and advanced imaging systems that can capture detailed surface characteristics across vast areas in minimal time. The integration of artificial intelligence (AI) and machine learning (ML) into mapping software further automates data analysis, reducing operational costs and enabling predictive maintenance. These innovations are not only improving the quality of data collected but are also facilitating the development of scalable solutions that can be deployed across diverse geographies and infrastructure types, from highways in developed nations to rural roads in emerging markets.

The market is also benefiting from increasing regulatory support and funding for smart infrastructure projects. Governments and transportation authorities are recognizing the value of drone-based solutions in enhancing road safety, optimizing maintenance budgets, and achieving sustainability objectives. Environmental monitoring agencies are leveraging albedo mapping to assess the impact of road materials on local microclimates and energy consumption. Furthermore, public-private partnerships are fostering innovation in drone-based surveying, leading to the development of customized solutions tailored to specific regional and sectoral needs. These collaborative initiatives are expected to further stimulate market growth and drive the adoption of drone-based albedo mapping as a standard practice in infrastructure management.

From a regional perspective, North America currently leads the global drone-based road surface albedo mapping market, accounting for the largest share in 2024. The region’s dominance is attributed to advanced technological infrastructure, significant investments in smart city projects, and proactive government policies supporting sustainable transportation. However, Asia Pacific is anticipated to exhibit the highest CAGR over the forecast period, driven by rapid urbanization, expanding transportation networks, and increasing government focus on environmental monitoring. Europe is also witnessing substantial growth, particularly in countries prioritizing climate action and infrastructure modernization. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by infrastructure development and rising adoption of drone technologies.

In 2020, under a State Government funded program to test the application of machine learning (ML) to government data, the Dept for Energy and Mining (DEM) and the Australian Institute of Machine Learning (AIML) ran a trial to test the capability... In 2020, under a State Government funded program to test the application of machine learning (ML) to government data, the Dept for Energy and Mining (DEM) and the Australian Institute of Machine Learning (AIML) ran a trial to test the capability of ML to predict rock outcrop across the state using a variety of different remote sensing datasets and existing mapping data from DEM’s geological mapping programs. The AIML developed a tool that works by taking locations of known, mapped outcrops (known as the ground truth) and remote sensing imagery and uses machine learning, specifically deep learning, to train a model that can predict the location of the ground truth from the remote sensing imagery. The tool can then take the same remote sensing imagery for the state of South Australia and predict the probable locations of outcrops across the entire state and also output a value that indicates the confidence in its predictions. The Automated Outcrop Prediction Package is the outcomes from this project. This file contains the results of running the outcrop prediction tool using the following datasets: orthoimagery (WMTS); radiometrics data; night time thermal (NTT) imagery; Shuttle Radar Topographic Mission digital terrain data (DTM); and valley bottom flatness (VBF) data.

Road Network Mapping from Multispectral Satellite Imagery: Leveraging Deep Learning and Spectral Bands Submitted to AGILE24 Abstract Updating road networks in rapidly changing urban landscapes is an important but difficult task, often challenged by the complexity and errors of manual mapping processes. Traditional methods that primarily use RGB satellite imagery struggle with obstacles in the environment and varying road structures, leading to limitations in global data processing. This paper presents an innovative approach that utilizes deep learning and multispectral satellite imagery to improve road network extraction and mapping. By exploring U-Net models with DenseNet backbones and integrating different spectral bands we apply semantic segmentation and extensive post-processing techniques to create georeferenced road networks. We trained two identical models to evaluate the impact of using images created from specially selected multispectral bands rather than conventional RGB images. Our experiments demonstrate the positive impact of using multispectral bands, by improving the results of the metrics Intersection over Union (IoU) by 6.5%, F1 by 5.4%, and the newly proposed relative graph edit distance (relGED) and topology metrics by 2.2% and 2.6% respectively. Data To use the code in this repository, download the required data from SpaceNet Challenge 3 (https://spacenet.ai/spacenet-roads-dataset/) via AWS. The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. SpaceNet was accessed on 05.01.2023 from https://registry.opendata.aws/spacenet Software The analysis and results of this research were achieved with Python and several software packages such as: - tensorflow - networkx - Pillow, cv2 - GDAL, rasterio, shapely - APLS For a fully reproducible environment and software versions refer to 'environment.yml'. All data is licensed under CC BY 4.0, all software files are licensed under the MIT License. Reproducibility To execute the scripts and train your model, first refer to the 'Data' section of this file to download the data from the providers. Apply the preprocessing steps from 'preprocessing.py', but consider that to avoid redundancy, preprocessing steps not included in this repository are the conversion of geojson road data into training images, the reduction of satellite images to an 8-bit format, and their conversion into '.png' files. These steps can be achieved by applying and, if necessary, modifying the APLS library which is publicly available under https://github.com/CosmiQ/apls. Apply preprocessing to both RGB and MS images. To generate the latter execute the 'ms_channel_seperation.py' script while specifying the wanted multispectral channels. Execute the 'train_model.py' script to train your semantic segmentation model, and apply post-processing procedures with 'postprocessing.py'. Generate the metrics results by executing 'evaluation.py'. To save storage space, not all the used data is made available in this repository. Please refer to the 'Data' section of this file to access and download the data from the providers. Exemplary preprocessed training data (100 split images of Las Vegas) is included in the folders './data/tiled512/small_test_sample/ms/' and './data/tiled512/small_test_sample/rgb/'. Post-processed results are provided in the corresponding folders './results/UNetDense_MS_512/' and './results/UNetDense_RGB_512/'. These include the stitched and recombined images, without any post-processing applied to them, as well as the extracted and post-processed graphs as '.pickle' files. This provided data was used to calculate the metrics Intersection over Union (IoU), F1 score, relGED, and topology metric as presented in the paper. The figures included in the paper can be reproduced by saving images created during the preprocessing, training, and post-processing steps. To generate the plots of resulting graphs, refer to the corresponding functions and enable the boolean parameter 'plot'. Bounding boxes seen in the figures were drawn manually and only serve an explanatory purpose. Please be advised that file paths and folder structure have to be adapted manually in the scripts to suit the users folder structure. Be aware of selecting uniform file paths and storing the results in folders named after their model. Furthermore, the code is not meant to be executed from the terminal, running the individual scripts in an IDE is recommended.

Genetic mapping is a basic tool necessary for anchoring assembled scaffold sequences and for identifying QTLs controlling important traits. Though bitter gourd (Momordica charantia) is both consumed and used as a medicinal, research on its genomics and genetic mapping is severely limited. Here, we report the construction of a restriction site associated DNA (RAD)-based genetic map for bitter gourd using an F2 mapping population comprising 423 individuals derived from two cultivated inbred lines, the gynoecious line ‘K44’ and the monoecious line ‘Dali-11.’ This map comprised 1,009 SNP markers and spanned a total genetic distance of 2,203.95 cM across the 11 linkage groups. It anchored a total of 113 assembled scaffolds that covered about 251.32 Mb (85.48%) of the 294.01 Mb assembled genome. In addition, three horticulturally important traits including sex expression, fruit epidermal structure, and immature fruit color were evaluated using a combination of qualitative and quantitative data. As a result, we identified three QTL/gene loci responsible for these traits in three environments. The QTL/gene gy/fffn/ffn, controlling sex expression involved in gynoecy, first female flower node, and female flower number was detected in the reported region. Particularly, two QTLs/genes, Fwa/Wr and w, were found to be responsible for fruit epidermal structure and white immature fruit color, respectively. This RAD-based genetic map promotes the assembly of the bitter gourd genome and the identified genetic loci will accelerate the cloning of relevant genes in the future.

The left-right asymmetry of snails, including the direction of shell coiling, is determined by the delayed effect of a maternal gene on the chiral twist that takes place during early embryonic cell divisions. Yet, despite being a well-established classical problem, the identity of the gene and the means by which left-right asymmetry is established in snails remain unknown. We here demonstrate the power of new genomic approaches for identification of the chirality gene, “D”. First, heterozygous (Dd) pond snails Lymnaea stagnalis were self-fertilised or backcrossed, and the genotype of more than six thousand offspring inferred, either dextral (DD/Dd) or sinistral (dd). Then, twenty of the offspring were used for Restriction-site-Associated DNA Sequencing (RAD-Seq) to identify anonymous molecular markers that are linked to the chirality locus. A local genetic map was constructed by genotyping three flanking markers in over three thousand snails. The three markers lie either side of the chirality locus, with one very tightly linked (

This layer is displayed on the Pacific Motorway service road types overlay map in City Plan version 7. The layer is also available in Council’s City Plan interactive mapping tool. For further informat...Show full descriptionThis layer is displayed on the Pacific Motorway service road types overlay map in City Plan version 7. The layer is also available in Council’s City Plan interactive mapping tool. For further information on City Plan, please visit http://www.goldcoast.qld.gov.au/planning-and-building/city-plan-2015-19859.html

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode