The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.

The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.

If you share or use this dataset, please cite [4] and [5] in any relevant documentation.

In addition, an image dataset for crack classification has also been published at [6].

References:

[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873

[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605

[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434

[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678

5 Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044

[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

This dataset tracks annual distribution of students across grade levels in Range View Elementary School

The Long-range Pedestrian Dataset is curated for the visual entertainment sector, featuring a collection of outdoor-collected images with a high resolution of 3840 x 2160 pixels. This dataset is focused on long-distance pedestrian imagery, with each target pedestrian precisely labeled with a bounding box that closely fits the boundary of the pedestrian target, providing detailed data for scene composition and character placement in visual content.

The dataset consists of 181 HDR images. Each image includes: 1) a RAW exposure stack, 2) an HDR image, 3) simulated camera images at two different exposures 4) Results of 6 single-image HDR reconstruction methods: Endo et al. 2017, Eilertsen et al. 2017, Marnerides et al. 2018, Lee et al. 2018, Liu et al. 2020, and Santos et al. 2020

Project web page More details can be found at: https://www.cl.cam.ac.uk/research/rainbow/projects/sihdr_benchmark/

Overview This dataset contains 181 RAW exposure stacks selected to cover a wide range of image content and lighting conditions. Each scene is composed of 5 RAW exposures and merged into an HDR image using the estimator that accounts photon noise 3. A simple color correction was applied using a reference white point and all merged HDR images were resized to 1920×1280 pixels.

The primary purpose of the dataset was to compare various single image HDR (SI-HDR) methods [1]. Thus, we selected a wide variety of content covering nature, portraits, cities, indoor and outdoor, daylight and night scenes. After merging and resizing, we simulated captures by applying a custom CRF and added realistic camera noise based on estimated noise parameters of Canon 5D Mark III.

The simulated captures were inputs to six selected SI-HDR methods. You can view the reconstructions of various methods for select scenes on our interactive viewer. For the remaining scenes, please download the appropriate zip files. We conducted a rigorous pairwise comparison experiment on these images to find that widely-used metrics did not correlate well with subjective data. We then proposed an improved evaluation protocol for SI-HDR [1].

If you find this dataset useful, please cite [1].

References [1] Param Hanji, Rafał K. Mantiuk, Gabriel Eilertsen, Saghi Hajisharif, and Jonas Unger. 2022. “Comparison of single image hdr reconstruction methods — the caveats of quality assessment.” In Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings (SIGGRAPH ’22 Conference Proceedings). [Online]. Available: https://www.cl.cam.ac.uk/research/rainbow/projects/sihdr_benchmark/

[2] Gabriel Eilertsen, Saghi Hajisharif, Param Hanji, Apostolia Tsirikoglou, Rafał K. Mantiuk, and Jonas Unger. 2021. “How to cheat with metrics in single-image HDR reconstruction.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops. 3998–4007.

[3] Param Hanji, Fangcheng Zhong, and Rafał K. Mantiuk. 2020. “Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration.” In Advances in Image Manipulation (ECCV workshop). Springer, 376–391. [Online]. Available: https://www.cl.cam.ac.uk/research/rainbow/projects/noise-aware-merging/

This dataset tracks annual total students amount from 2013 to 2023 for Range View Elementary School

About this dataset

Context

The dataset tabulates the population of Grass Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Grass Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a considerable majority of female population, with 71.13% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Grass Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Grass Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Race & Ethnicity. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Palm Springs, CA.

This dataset tracks annual total classroom teachers amount from 2013 to 2023 for Range View Elementary School

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

• A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
  • 30 completely labeled (segmented) images
  • 71 partly labeled images
  • altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)
• To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects
• A set of metrics and a novel ranking score for respective meaningful method benchmarking
• An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

>> FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

• fisbe_v1.0_{completely,partly}.zip
  • contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.
• fisbe_v1.0_mips.zip
  • maximum intensity projections of all samples, for convenience.
• sample_list_per_split.txt
  • a simple list of all samples and the subset they are in, for convenience.
• view_data.py
  • a simple python script to visualize samples, see below for more information on how to use it.
• dim_neurons_val_and_test_sets.json
  • a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.
• Readme.md
  • general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.
For each image, we provide a pixel-wise instance segmentation for all separable neurons.
Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").
The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.
The segmentation mask for each neuron is stored in a separate channel.
The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9
conda activate flylight-env

How to open zarr files

1. Install the python zarr package:

   pip install zarr

2. Opened a zarr file with:
   

   import zarr
raw = zarr.open(
seg = zarr.open(

   # optional:
import numpy as np
raw_np = np.array(raw)

Zarr arrays are read lazily on-demand.
Many functions that expect numpy arrays also work with zarr arrays.
Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

1. Install napari:

   pip install "napari[all]"

2. Save the following Python script:
   

   import zarr, sys, napari

   raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")
gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

   viewer = napari.Viewer(ndisplay=3)
for idx, gt in enumerate(gts):
viewer.add_labels(
gt, rendering='translucent', blending='additive', name=f'gt_{idx}')
viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')
viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')
viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')
napari.run()

3. Execute:

   python view_data.py 

Metrics

• S: Average of avF1 and C
• avF1: Average F1 Score
• C: Average ground truth coverage
• clDice_TP: Average true positives clDice
• FS: Number of false splits
• FM: Number of false merges
• tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..
For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe,
 title =    {FISBe: A real-world benchmark dataset for instance
         segmentation of long-range thin filamentous structures},
 author =    {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
         Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
         Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
 year =     2024,
 eprint =    {2404.00130},
 archivePrefix ={arXiv},
 primaryClass = {cs.CV}
}

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable
discussions.
P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.
This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.
All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

About this dataset

Context

The dataset tabulates the population of South Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of South Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.64% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the South Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of South Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Race & Ethnicity. You can refer the same here

This dataset tracks annual overall school rank from 2011 to 2022 for Range View Elementary School

This dataset provides information about the number of properties, residents, and average property values for Range View Drive cross streets in Bailey, CO.

This dataset tracks annual math proficiency from 2011 to 2022 for Range View Elementary School vs. Colorado and Weld County Reorganized School District No. Re-4

In support of new permitting workflows associated with anticipated WellSTAR needs, the CalGEM GIS unit extended the existing BLM PLSS Township & Range grid to cover offshore areas with the 3-mile limit of California jurisdiction. The PLSS grid as currently used by CalGEM is a composite of a BLM download (the majority of the data), additions by the DPR, and polygons created by CalGEM to fill in missing areas (the Ranchos, and Offshore areas within the 3-mile limit of California jurisdiction).CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).Update Frequency: As Needed

Historical Dataset of Range View Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2013-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2013-2023),American Indian Student Percentage Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2021-2022),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2019-2022),White Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (2013-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Overall School Rank Trends Over Years (2011-2022)

Shows areas where the health and prevalence of caribou can be linked to the attributes of the land that supports them. Ontario's Woodland Caribou Recovery Strategy (2008) provides advice and recommendations on the approaches needed for the recovery of Woodland Caribou. The strategy recommends the identification of ranges and local populations to: * maintain existing, self-sustaining, genetically-connected local populations of caribou * ensure security for and (reproductive) connections among currently isolated mainland caribou * re-establish caribou in strategic areas to create self-sustaining local populations and ensure connectivity Instructions for downloading this dataset: * select the link below and scroll down the metadata record page until you find Transfer Options in the Distribution Information section, * select the link beside the Data for download label, * you must provide your name, organization and email address in order to access the dataset This product requires the use of GIS software. *[GIS]: geographic information system

This dataset provides information about the number of properties, residents, and average property values for Range View Cove cross streets in Austin, TX.

Suspension Rate by Grade Range reports the percentage of students receiving at least one sanction (ISS, OSS, EXP) by grade range.

Project Goals: To identify regions of recently evolved endemic (neo-endemism) mammal species in California and thereby infer areas of rapid evolutionary diversification, which may help guide conservation prioritization and future planning for protected areas. Four species-based GIS rasters were produced of mammalian endemism (see reference for details ). This is: Richness of species distribution models weighted by inverse range size Abstract: The high rate of anthropogenic impact on natural systems mandates protection of the evolutionary processes that generate and sustain biological diversity. Environmental drivers of diversification include spatial heterogeneity of abiotic and biotic agents of divergent selection, features that suppress gene flow, and climatic or geological processes that open new niche space. To explore how well such proxies perform as surrogates for conservation planning, we need first to map areas with rapid diversification — ‘evolutionary hotspots’. Here we combine estimates of range size and divergence time to map spatial patterns of neo-endemism for mammals of California, a global biodiversity hotspot. Neo-endemism is explored at two scales: (i) endemic species, weighted by the inverse of range size and mtDNA sequence divergence from sisters; and (ii) as a surrogate for spatial patterns of phenotypic divergence, endemic subspecies, again using inverse-weighting of range size. The species-level analysis revealed foci of narrowly endemic, young taxa in the central Sierra Nevada, northern and central coast, and Tehachapi and Peninsular Ranges. The subspecies endemism-richness analysis supported the last four areas as hotspots for diversification, but also highlighted additional coastal areas (Monterey to north of San Francisco Bay) and the Inyo Valley to the east. We suggest these hotspots reflect the major processes shaping mammal neo-endemism: steep environmental gradients, biotic admixture areas, and areas with recent geological/climate change. Anthropogenic changes to both environment and land use will have direct impacts on regions of rapid divergence. However, despite widespread changes to land cover in California, the majority of the hotspots identified here occur in areas with relatively intact ecological landscapes. The geographical scope of conserving evolutionary process is beyond the scale of any single agency or nongovernmental organization. Choosing which land to closely protect and/or purchase will always require close coordination between agencies. Citation:DAVIS, E.B., KOO, M.S., CONROY, C., PATTON, J.L. & MORITZ, C. (2008) The California Hotspots Project: identifying regions of rapid diversification of mammals. Molecular Ecology 17, 120 -138. This dataset was reviewed in another manner. Spatial Resolution: 0.0083333338 DD This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

This point feature class contains 81,481 points arranged in a 270-meter spaced grid that covers the Spring Mountains and Sheep Range in Clark County, Nevada. Points are attributed with hydroclimate variables and ancillary data compiled to support efforts to characterize ecological zones.