Download US Geological Survey topographic maps in multiple formats, scales, and years, including 1:24,000-scale topo maps, using the USGS topoView web application.Learn how to use topoView: https://youtu.be/UCTIvQqVr4E

San Joaquin Valley Subsidence Analysis README.
Written: Joel Dudas, 3/12/2017. Amended: Ben Brezing, 4/2/2019. DWR’s Division of Engineering Geodetic Branch received a request in 1/2017 from Jeanine Jones to produce a graphic of historic subsidence in the entirety of the San Joaquin Valley. The task was assigned to the Mapping & Photogrammetry Office and the Geospatial Data Support Section to complete by early February. After reviewing the alternatives, the decision was made to produce contours from the oldest available set of quad maps for which there was reasonable certainty about quality and datum, and to compare that to the most current Valley-wide DEM. For the first requirement, research indicated that the 1950’s vintage quad maps for the Valley were the best alternative. Prior quad map editions are uneven in quality and vintage, and the actual control used for the contour lines was extremely suspect. The 1950’s quads, by contrast, were produced primarily on the basis of 1948-1949 aerial photography, along with control corresponding to that period, and referenced to the National Geodetic Vertical Datum of 1929. For the current set, the most recent Valley-wide dataset that was freely available, in the public domain, and of reasonable accuracy was the 2005 NextMap SAR acquisition (referenced to NAVD88). The primary bulk of the work focused on digitizing the 1950’s contours. First, all of the necessary quads were downloaded from the online USGS quad source https://ngmdb.usgs.gov/maps/Topoview/viewer/#4/41.13/-107.51. Then the entire staff of the Mapping & Photogrammetry Lab (including both the Mapping Office and GDDS staff) proceeded to digitize the contours. Given the short turnaround time constraint and limited budget, certain shortcuts occurred in contour development. While efforts were made to digitize accurately, speed really was important. Contours were primarily focused only on agricultural and other lowland areas, and so highlands were by and large skipped. The tight details of contours along rivers, levees, and hillsides was skipped and/or simplified. In some cases, only major contours were digitized. The mapping on the source quads itself varied….in a few cases on spot elevations on benchmarks were available in quads. The contour interval sometimes varied, even within the quad sheet itself. In addition, because 8 different people were creating the contours, variability exists in the style and attention to detail. It should be understood that given the purpose of the project (display regional subsidence patterns), that literal and precise development of the historic contour sets leaves some things to be desired. These caveats being said, the linework is reasonably accurate for what it is (particularly given that the contours of that era themselves were mapped at an unknown and varying actual quality). The digitizers tagged the lines with Z values manually entered after linework that corresponded to the mapped elevation contours. Joel Dudas then did what could be called a “rough” QA/QC of the contours. The individual lines were stitched together into a single contour set, and exported to an elevation raster (using TopoToRaster in ArcGIS 10.4). Gross blunders in Z values were corrected. Gaps in the coverage were filled. The elevation grid was then adjusted to NAVD88 using a single adjustment for the entire coverage area (2.5’, which is a pretty close average of values in this region). The NextMap data was extracted for the area, and converted into feet. The two raster sets were fixed to the same origin point. The subsidence grid was then created by subtracting the old contour-derived grid from the NextMAP DEM. The subsidence grid that includes all of the values has the suffix “ALL”. Then, to improve the display fidelity, some of the extreme values (above +5’ and below -20’*) were filtered out of the dataset, and the subsidence grid was regenerated for these areas and suffixed with “cut.” The purpose of this cut was to extract some of the riverine and hilly areas that produced more extreme values and other artifacts purely due to the analysis approach (i.e. not actual real elevation change). * - some of the areas with more than 20 feet of subsidence were omitted from this clipping, because they were in heavily subsided areas and may be “real subsidence.”The resulting subsidence product should be perceived in light of the above. Some of the collar of the San Joaquin Valley shows large changes, but that is simply due to the analysis method. Also, individual grid cells may or may not be comparing the same real features. Errors are baked into both comparison datasets. However, it is important to note that the large areas of subsidence in the primary agriculture area agree fairly well with a cruder USGS subsidence map of the Valley based on extensometer data. We have confidence that the big picture story these results show us is largely correct, and that the magnitudes of subsidence are somewhat reasonable. The contour set can serve as the baseline to support future comparisons using more recent or future data as it becomes available. It should be noted there are two key versions of the data. The “Final Deliverables” from 2/2017 were delivered to support the initial Public Affairs press release. Subsequent improvements were made in coverage and blunder correction as time permitted (it should be noted this occurred in the midst of the Oroville Dam emergency) to produce the final as of 3/12/2017. Further improvements in overall quality and filtering could occur in the future if time and needs demand it.
Update (4/3/2019, Ben Brezing): The raster was further smoothed to remove artifacts that result from comparing the high resolution NextMAP DEM to the lower resolution DEM that was derived from the 1950’s quad map contours. The smoothing was accomplished by removing raster cells with values that are more than 0.5 feet different than adjacent cells (25 meter cell size), as well as the adjacent cells. The resulting raster was then resampled to a raster with 100 meter cell size using cubic resampling technique and was then converted to a point feature class. The point feature class was then interpolated to a raster with 250 meter cell size using the IDW technique, a fixed search radius of 1250 meters and power=2. The resulting raster was clipped to a smaller extent to remove noisier areas around the edges of the Central Valley while retaining coverage for the main area of interest.

Input topographic maps, surface mine extents, and quad boundaries used in the following study:Maxwell, A.E., M.S. Bester, L.A. Guillen, C.A. Ramezan, D.J. Carpinello, Y. Fan, F.M. Hartley, S.M. Maynard, and J.L. Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 1-25. https://doi.org/10.3390/rs12244145.Associated code and descriptions of the data are provided on GitHub: https://github.com/maxwell-geospatial/topoDL. The surface mine extent data were obtained from the USGS prospect- and mine-related features from USGS topographic maps dataset: https://mrdata.usgs.gov/usmin/. Topographic maps were downloaded from TopoView/The National Map. We have simply prepared the data for easier ingestion into deep learning semantic segmentation workflows by aligning the vector polygon data with the associated topographic map and including topographic map boundaries to remove the collar information. Vector data can be rasterized and combined with the topographic maps to generate image chips and masks for semantic segmentation deep learning.The chip prep script on GitHub can be used to create chips and masks from these data. This compressed folder contains the following subfolders (ky_mines, ky_quads, ky_topos, oh_mines, oh_quads, oh_topos, va_mines, va_quads, va_topos). The mines folders contain the mine extents for each topographic map used in the study while the quads folders contain the quadrangle boundaries. All vector data are in shapefile format. The topos folders contain the topographic maps in TIFF format.

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Analysis of historical maps and Landsat imagery suggests coastal glaciers in the western Prince William Sound have retreated since the end of the Little Ice Age, with a period of accelerated retreat after 2004/06. I develop a multi-temporal inventory of 43 glaciers based on historical field observations, topographic maps, and Landsat imagery. Area and length measurements are derived from digitized outlines, and center lines calculated using a semi-automatic, geographic information system-based algorithm. Land-based glaciers retreated at a rate of 22 m a-1 from ~1950 to 2004/06 and peaked to 48 m a-1 after 2004/06. From ~1950 to 2018, the total area of land-based glaciers decreased by 228 km2, with 36% of the glacier loss occurring after 2004/06. Tidewater glaciers reacted asynchronously compared to land-based glaciers, with differing rates of area and length loss. Evaluation of climate trends indicates increasing temperatures and decreasing winter precipitation in the study area.

Historical topographic maps of the study area provide the spatial data needed to extend glacier length change and area chronologies to the 1950s. The 21 maps I obtained for this study are available for download in a georeferenced format from the USGS (https://ngmdb.usgs.gov/topoview/viewer/#4/40.00/-100.00), allowing for use in geographical information systems (GIS) without further processing. The maps span 1951-1960 and are produced at the 1:63,360 scale from aerial photographs acquired from 1948-1957. I access Landsat images from an online service portal (ESRI, 2019). The images are georeferenced and orthorectified by USGS, allowing for direct integration into GIS. The images, at 30-60 m resolution, provide the spatial data for the repeat measurement of glacier outlines spanning 1973-2018. Previous studies provided Little Ice Age maximums for eight of the land-based glaciers analyzed in this study (Barclay et al., 2003; Wiles et al., 1999).

I manually digitize outlines from historical maps, topographic maps, and Landsat images for glaciers 10 km2 or larger. Each study glacier is identified by a project identification number; Global Land Ice Measurements (GLIMS) and Randolph Glacier Inventory (RGI) identification numbers; and glacier name, if available. I manually digitize and adjust glacier boundaries based on the interpretation of 1950/57 topographic maps and Landsat images acquired in 1973/75, 1986, 1994, 2004/06, and 2018. Glacier length changes are measured from the intersection of the center line with each glacier terminus. I repeat measurements for 1950/57 topographic maps and the Landsat images acquired in 1973/75, 1986, 1994, 2004/06, and 2018, resulting in a glacier length change chronology for each glacier. For a subset of eight glaciers, I measure length changes to the digitized LIA maximum terminus positions identified in previous studies.

National Pipeline Mapping System: https://pvnpms.phmsa.dot.gov/PublicViewer/TC Energy PDF Map: https://www.tcenergy.com/siteassets/pdfs/natural-gas/gtnxp/tce-gas-transmission-northwest-xpress-map.pdfCompressor data HIFLD (https://ft.maps.arcgis.com/home/item.html?id=d910e5aca7434d19899b1e5a05234051)USGS Topo Maps: https://ngmdb.usgs.gov/topoview/viewer/#4/40.00/-100.00Aerial Imagery:Historical - Google Earth Pro (using the time slider to check for ground scars over the years)Bing Satellite Imagery QGIS Plugin

The AZ USGS 1:24k Topo Maps web app displays the geographic extent of the USGS 1:24,000 quandrangles to aid in identifying and requesting topographic maps from the ASU Library Map and Geospatial Hub.Geographic Coverage: Arizona, United StatesTime Range: 1958-1987, 1992, 2010Cartographic Scale: 1:24,000Physical Availability: in-house use onlyDigital Availability: see topoView

Soil Conservation Service (SCS) aerial photographs of portions of Arizona, by Fairchild Inc. in partnership with the SCS:Geographic coverage: ArizonaTime range: 1935-1937, 1951Cartographic scale: 1:62,500 and 1:15,131Physical availability: in-house use onlyDigital availability: scanned (600dpi)Once you have identified your photos of interest, please submit a quick service request through the Map and Geospatial Hub's Service Request form.When available, one can identify the corresponding USGS topographic map for each cell in the index grid by referencing the value for that cell's 'topo name'. Those topographic maps can then be accessed directly via USGS topoView.