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NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗
NASA NTRS2012

Improving the Inventory of Large Lunar Basins: Using LOLA Data to Test Previous Candidates and Search for New Ones

Topography and crustal thickness data from LOLA altimetry were used to test the validity of 98 candidate large lunar basins derived from photogeologic and earlier topographic and crustal thickness data, and to search for possible new candidates. We eliminate 23 previous candidates but find good evidence for 20 new candidates. The number of basins greater than 300 km diameter on the Moon is almost certainly a factor 2 (maybe 3?) larger than the number of named features having basin-like topography. Unified Lunar Control Net 2005 data [1] and model crustal thickness data [2] were previously used to search for possible previously unrecognized large lunar impact basins [3,4]. An inventory of 98 candidate topographic basins greater than 300 km in diameter was found [5]. This includes 33 named features (only those having basin-like topography) out of the 45 listed by Wilhelms [6], 38 additional Quasi-Circular Depressions (QCDs) found in the ULCN2005 topography, and 27 Circular Thin Areas (CTAs) found in model crustal thickness data [2]. Most named features and additional QCDs have strong CTA signatures, but there may be a class of CTAs that are not easily recognized in the old and low resolution ULCN2005 topography. Lunar Orbiter Laser Altimeter (LOLA) data have recently become publically available. We used these data to (a) refine the center and ring diameters of known basins, (b) test the viability of the candidate basins previously found (as described above), and (c) search for additional candidate basins not revealed by the earlier lower resolution data. We used the LOLA topography directly but also a recent new model crustal thickness data that includes Kaguya gravity data [7]. We repeated a Topographic Expression (TE) and a Crustal Thickness Expression (CTE) scoring exercise originally done with the basins found in ULCN and earlier model crustal thickness data [5]. Each candidate was scored on a scale from 0 (no topographic basin or circular thin area signature) to 5 (strong circular low or strong circular thin area signature). These were combined into a total score used to rank the probability for each candidate basin. We used the same GRIDVIEW software to stretch, contour and profile the LOLA and new crustal thickness data as was done with the ULCN2005 and older model crustal thickness data.

Frey, H. V.↗
NASA NTRS2012

Preliminary Crater Retention Ages for an Expanded Inventory of Large Lunar Basins

Based on LOLA topography and a new crustal thickness model, the number of candidate lunar basins greater than 300 km in diameter is at least a factor 2 larger than the traditional number based on photogeology alone, and may be as high as 95. Preliminary N(50) crater retention ages for this population of candidate basins shows two distinct peaks. Frey [1] suggested, based on Clementine-era topography (ULCN2005) and a crustal thickness model based on Lunar Prospector data [2], that there could be as many as 98 lunar basins greater than 300 km diameter. Many of the weaker cases have not stood up to recent testing [3,4,5] using LOLA data and a newer crustal thickness model based on Kaguya gravity data and LOLA topography data [6]. As described in companion abstracts [4,5], we have deleted from the earlier inventory 1 more named feature (Sikorsky- Rittenhouse; LOLA data show that its diameter is actually less than 300 km), 11 Quasi-Circular Depressions (QCDs) identified in the ULCN topography, and 11 Circular Thin Areas (CTAs) found in the earlier crustal thickness model [2]. We did this by repeating the scoring exercise originally done in [1] but with the new data [4,5]. Topographic Expression (TE) and Crustal Thickness Expression (CTE) scores were determined for each candidate on a scale of 0 to 5 (5 being a strong, circular signature, 0 for those with no discernible circular topographic or crustal thickness signature). These scores are added together to produce a Summary Score which has a range of 0 to 10. We eliminated all candidates with a Summary Score less than 3, as well as other cases where, for example, the TE went to zero because what looked like a single large circular QCD in the lower resolution ULCN data was in fact a cluster of smaller deep impacts readily apparent in the newer higher resolution LOLA data. This process reduced the original inventory from 98 to 75 candidates.

Frey, H. V.↗
NASA NTRS1976

Gravity anomaly detection: Apollo/Soyuz

The Goddard Apollo-Soyuz Geodynamics Experiment is described. It was performed to demonstrate the feasibility of tracking and recovering high frequency components of the earth's gravity field by utilizing a synchronous orbiting tracking station such as ATS-6. Gravity anomalies of 5 MGLS or larger having wavelengths of 300 to 1000 kilometers on the earth's surface are important for geologic studies of the upper layers of the earth's crust. Short wavelength Earth's gravity anomalies were detected from space. Two prime areas of data collection were selected for the experiment: (1) the center of the African continent and (2) the Indian Ocean Depression centered at 5% north latitude and 75% east longitude. Preliminary results show that the detectability objective of the experiment was met in both areas as well as at several additional anomalous areas around the globe. Gravity anomalies of the Karakoram and Himalayan mountain ranges, ocean trenches, as well as the Diamantina Depth, can be seen. Maps outlining the anomalies discovered are shown.

Vonbun, F. O.↗
NASA NTRS1989

Buried topography of Utopia, Mars - Persistence of a giant impact depression

Knobs, partially buried craters, ring fractures, and some mesas permit a qualitative determination of the topography buried beneath younger northern plains materials. These features are widely distributed in the Utopia area but are absent in a large, roughly circular region centered at about 48 deg N, 240 deg W. This implies the existence of a circular depression about 3300 km in diameter buried beneath Utopia Planitia that is interpreted to represent the central part of a very large impact basin. The presence of buried curved massifs around part of this depression, and a roughly coincident mascon, lend further support. Present topography, areal geology, and paleotopography of buried surfaces all point to the persistence of this major depression for almost the entire history of Mars.

Mcgill, George E.↗
NASA NTRS1981

NASA's oceanic remote sensing plans for the 1980's

An overview of NASA's oceanic remote sensing plans is given, focusing on an attempt to obtain time-varying three-dimensional structure data of the ocean. The first mission is the National Oceanic Satellite System, which is designed to perform an operational demonstration of global sea surface monitoring for NOAA and DOD. TOPEX is the second mission, and it is designed to observe the surface topography of the ocean in order to determine the absolute geostrophic velocity field of the ocean. The third mission, Ocean SAR, has not been defined, but the areas of physical interpretation of oceanic imagery, selection of optimal frequency and depression angle for specific observational needs, and real-time image processing must be considered. It is hoped that oceanic satellite missions will be optimally exploited by the end of the decade.

Wilson, W. S.↗
NASA NTRS1999

Discovery of a New Cassini-Size Basin on Mars from MOLA Topographic Data

MOLA profile data collected during the Science Phasing Operations have revealed a previously unknown, 450 km wide, 2 km deep basin on Mars centered at 30N, 312W near the Phison Rupes. This structure, as large as and somewhat deeper than the very obvious Cassini impact basin located 1000 km to the SW, is not apparent in the existing good quality Viking imagery. Gridded MOLA data show the basin as a closed depression, but elevation contours show only weak correlation with what little structure exists in the area and with mapped geologic units. From analysis of slope breaks readily visible in two MOLA profiles we suggest this is at least a three-ring basin. Portions of rings from concentric fits to slope breaks align with some of the linear ridge-like structures of the Phison Rupes, and outline a region of lower crater density and smoother inter-crater plains. The discovery of such a pronounced topographic depression which lacks obvious visible structure suggests more such previously unknown features may exist on Mars, and that MOLA data may be useful in finding them.

Frey, H.↗
NASA NTRS1999

Fluid shear stress as a regulator of gene expression in vascular cells: possible correlations with diabetic abnormalities

Diabetes mellitus is associated with increased frequency, severity and more rapid progression of cardiovascular diseases. Metabolic perturbations from hyperglycemia result in disturbed endothelium-dependent relaxation, activation of coagulation pathways, depressed fibrinolysis, and other abnormalities in vascular homeostasis. Atherosclerosis is localized mainly at areas of geometric irregularity at which blood vessels branch, curve and change diameter, and where blood is subjected to sudden changes in velocity and/or direction of flow. Shear stress resulting from blood flow is a well known modulator of vascular cell function. This paper presents what is currently known regarding the molecular mechanisms responsible for signal transduction and gene regulation in vascular cells exposed to shear stress. Considering the importance of the hemodynamic environment of vascular cells might be vital to increasing our understanding of diabetes.

Review, Tutorial↗
NASA NTRS2012

Using GRIDVIEW to Better Understand the Early Bombardment History of the Moon, Mars and Earth

For more than a decade we have used GRIDVIEW to help analyze topographic and related data for Mars and more recently for the Moon. Our focus has been to employ the stretching, contouring, profiling, circle-fitting and other capabilities of GRIDVIEW to search for Quasi-Circular Depressions (CTAs) in MOLA, LOLA and other topographic data, and for Circular Thin Areas (CTAs) in Mars and Moon model crustal thickness data. Both QCDs and CTAs likely represent buried or obscured impact craters not readily visible in image data. We found clear evidence for a much larger population of buried impact craters in the northern lowlands of Mars (Frey et al. 2002), suggesting that part of the Red Planet is not significantly younger than the southern highlands. Edgar and Frey (2008) found that the N(300) crater retention ages of both areas were essentially identical, a conclusion confirmed by Wyatt (unpublished data) using more recent crustal thickness data for Mars. MOLA topographic data and MOLA-derived crustal thickness data were used to both identify a large number of previously unrecognized very large impact basins (D> 1000 km) on Mars and to determine relative crater retention ages for them (Frey, 2008). The distribution of N(300) CRAs suggested most formed in a relatively short interval of time. This dating also suggested the main magnetic field of Mars disappeared during this period (Lillis et al., 2008), because only the youngest basins systematically lack a remagnetized signature. Similar QCD and CTA analysis of first Clementine (Frey, 2011) and more recently LOLA topographic and LOLA-derived crustal thickness data for the Moon (Frey et al., 2011) revealed a significantly larger population of impact basins > 300 km in diameter than previously known. N(50) CRAs suggest a two-peak distribution of ages (Frey, 2012). An improved counting process confirms the two peaks, perhaps indicating both a pre-Nectaris Early Heavy Bombardment (EHB) as well as a Late Heavy Bombardment (LHB) on the Moon (Frey and Burgess, 2012, this meeting), with obvious implications for the early bombardment history of the Earth.

Frey, Herbert↗
NASA NTRS2010

Supercapacitor Electrolyte Solvents with Liquid Range Below -80 C

A previous NASA Tech Brief ["Low-Temperature Supercapacitors" (NPO-44386) NASA Tech Briefs, Vol. 32, No 7 (July 2008), page 32] detailed ongoing efforts to develop non-aqueous supercapacitor electrolytes capable of supporting operation at temperatures below commercially available cells (which are typically limited to charging and discharging at > or equal to -40 C). These electrolyte systems may enable energy storage and power delivery for systems operating in extreme environments, such as those encountered in the Polar regions on Earth or in the exploration of space. Supercapacitors using these electrolytes may also offer improved power delivery performance at moderately low temperatures (e.g. -40 to 0 C) relative to currently available cells, offering improved cold-cranking and cold-weather acceleration capabilities for electrical or hybrid vehicles. Supercapacitors store charge at the electrochemical double-layer, formed at the interface between a high surface area electrode material and a liquid electrolyte. The current approach to extending the low-temperature limit of the electrolyte focuses on using binary solvent systems comprising a high-dielectric-constant component (such as acetonitrile) in conjunction with a low-melting-point co-solvent (such as organic formates, esters, and ethers) to depress the freezing point of the system, while maintaining sufficient solubility of the salt. Recent efforts in this area have led to the identification of an electrolyte solvent formulation with a freezing point of -85.7 C, which is achieved by using a 1:1 by volume ratio of acetonitrile to 1,3-dioxolane

Brandon, Erik↗
NASA NTRS1978

Up-date of traveling wave tube improvements

A brief survey is presented of areas of progress on traveling wave tube designs. Data demonstrates the effect of multistage depressed collectors, the design of which is made possible by powerful NASA computer programs. Other topics include beam refocusing, RF circuit losses, and cathode testing.

Buck, E.↗
NASA NTRS1977

Geologic interpretation of new observations of the surface of Venus

New radar observations of the surface of Venus provide further evidence of a diverse and complex geologic evolution. The radar bright feature 'Beta' (24 deg N, 85 deg W) is seen to be a 700 km diameter region elevated a maximum of approximately 10 km relative to its surroundings with a 60 x 90 km wide depression at its summit. 'Beta' is interpreted to be a large volcanic construct, analogous to terrestrial and Martian shield volcanoes. Two large, quasi-circular areas of low reflectivity, examples of a class of features interpreted to be impact basins by previous investigators who were without the benefit of actual topographic information, are shown in altimetry maps to be depressions. Thus the term 'basin' can be applied, although we urge a non-genetic usage until more complete understanding of their origin is achieved through analysis of future observations.

Saunders, R. S.↗
NASA NTRS1977

Endogenic modification of impact craters on Mercury

The presence of internally modified impact craters on Mercury's surface may be used to evaluate the possibility of Mercurian volcanism. Such craters are similar to the floor-fractured and mare-filled craters observed on the moon. Mariner-10 images show that most such craters occur, as on the moon, near plains-filled basins. Color-ratio images have indicated that some Mercurian craters manifest red plains materials on their floors. These features may be associated with lava analogous to mare basalts in some lunar craters, or with compositionally distinct subsurface material preserved within the impact crater. Several basins manifest photometric contrasts between basin exteriors and basin-filling plains. Dark haloes are observed around some impact craters superimposed on the interior plains. This suggests the excavation of compositionally distinct material. Some possible endogenic features are discerned, despite the poor surface resolution, such as irregular rimless depressions. It is felt that volcanism may have occurred on Mercury, and that in some areas it may be similar to that of the lunar Mare Australe region.

Schultz, P. H.↗
NASA NTRS2020

A Fresh Look at the Stratigraphy of Northern Australe

The roughly circular collection of mare deposits centered at ~38.9°S, 93°E is often re- ferred to as Mare Australe. It is located outside of the Procellarum KREEP Terrain. The circular arrangement of Australe’s mare patches has suggested an ancient, heavily degraded or relaxed impact basin roughly 900 km in diameter. The mare deposits are generally thought to have erupted into smaller post- basin craters. The type, volume, and distribution of mare eruptions potentially resembles the early stages of basin-filling mare events, but which are preserved in Australe and some farside locations. Gravity data suggest that if there was a basin, it is much smaller than originally proposed (now ~600 km) and located in the northern part of Mare Australe, between Humboldt, Milne, and Jenner craters. As a whole, Mare Australe lacks the topography typically associated with a basin; however, northern Australe has a slight topographic depression that roughly corresponds to the basin-like Bouguer gravity signature in the same area. The compositions exposed in Humboldt crater suggest that a preexisting basin might have excavated deeper crustal material. However, the underlying cause of the circularity of Mare Australe’s deposits, particularly those extending outside of the potential impact basin setting, is not yet understood. Thus, Australe may preserve fundamental information about mare volcanism potentially uncoupled from basin formation and structure. The objectives of this study are to use new high- resolution data (images, gravity, topography, and com- position) to reassess Australe’s mare deposits, deter- mine the timing and style of volcanism, identify discrete basalt deposits, and to further characterize the evolution of magmatism and subsurface structure in this area. Here, we focus on the northern Australe deposits (between Humboldt, Jenner, and Milne). As originally noted by Whitford-Stark (1979), Humboldt crater and its ejecta make an excellent stratigraphic marker that can be traced across much of the Australe region. The ejecta serves as a stratigraphic constraint for absolute model ages (AMAs) derived from crater size-frequency distributions (CSFDs).

Stopar, J. D.↗
NASA NTRS1973

Studies of the inner shelf and coastal sedimentation environment of the Beaufort Sea from ERTS-1

The author has identified the following significant results. Northward flowing rivers of Alaska inundate extensive areas of sea ice during spring breakup. This process has been studied under the ERTS-1 program. Drainage of large volumes of fresh water through the ice at holes and cracks (strudel) causes scour depressions, over 4 m deep, and up to 20 m across in the sea floor below. Strudel scours occur within 30 km of river mouths, generally in areas where ERTS-1 imagery shows less potential for drifting ice to scour the bottom than elsewhere. The shapes and distribution patterns of strudel scours correspond with those of strudel seen in the ice canopy. Densities of scours are highest in the inner areas of overlfow. But strudel scours also occur outside of overflow areas mapped during the last several years. These must be very old. One strudel scour investigated by diving is surrounded by a rim, has vertical walls exposing a tundra horizon, and terminates at a gravel layer 4 m below the lagoon floor. Another terminates at a semi-consolidated layer of silty clay. The gravel and silty clay are pre-Holocene deposits. Mixing of Holocene marine with older sediments by vertical strudel flow causes great variability in sediment types over small areas. These observations complicate interpretation of shallow water deposits of cold climates.

Reimnitz, E.↗
NASA NTRS1976

Fault tectonics and earthquake hazards in parts of southern California

The author has identified the following significant results. Four previously unknown faults were discovered in basement terrane of the Peninsular Ranges. These have been named the San Ysidro Creek fault, Thing Valley fault, Canyon City fault, and Warren Canyon fault. In addition fault gouge and breccia were recognized along the San Diego River fault. Study of features on Skylab imagery and review of geologic and seismic data suggest that the risk of a damaging earthquake is greater along the northwestern portion of the Elsinore fault than along the southeastern portion. Physiographic indicators of active faulting along the Garlock fault identifiable in Skylab imagery include scarps, linear ridges, shutter ridges, faceted ridges, linear valleys, undrained depressions and offset drainage. The following previously unrecognized fault segments are postulated for the Salton Trough Area: (1) An extension of a previously known fault in the San Andreas fault set located southeast of the Salton Sea; (2) An extension of the active San Jacinto fault zone along a tonal change in cultivated fields across Mexicali Valley ( the tonal change may represent different soil conditions along opposite sides of a fault). For the Skylab and LANDSAT images studied, pseudocolor transformations offer no advantages over the original images in the recognition of faults in Skylab and LANDSAT images. Alluvial deposits of different ages, a marble unit and iron oxide gossans of the Mojave Mining District are more readily differentiated on images prepared from ratios of individual bands of the S-192 multispectral scanner data. The San Andreas fault was also made more distinct in the 8/2 and 9/2 band ratios by enhancement of vegetation differences on opposite sides of the fault. Preliminary analysis indicates a significant earth resources potential for the discrimination of soil and rock types, including mineral alteration zones. This application should be actively pursued.

Merifield, P. M.↗
NASA NTRS1990

Synthetic Aperture Radar Imagery of Airports and Surrounding Areas: Denver Stapleton International Airport

This is the third in a series of three reports which address the statistical description of ground clutter at an airport and in the surrounding area. These data are being utilized in a program to detect microbursts. Synthetic aperture radar (SAR) data were collected at the Denver Stapleton Airport using a set of parameters which closely match those which are anticipated to be utilized by an aircraft on approach to an airport. These data and the results of the clutter study are described. Scenes of 13 x 10 km were imaged at 9.38 GHz and HH-, VV-, and HV-polarizations, and contain airport grounds and facilities (up to 14 percent), cultural areas (more than 50 percent), and rural areas (up to 6 percent). Incidence angles range from 40 to 84 deg. At the largest depression angles the distributed targets, such as forest, fields, water, and residential, rarely had mean scattering coefficients greater than -10 dB. From 30 to 80 percent of an image had scattering coefficients less than -20 dB. About 1 to 10 percent of the scattering coefficients exceeded 0 dB, and from 0 to 1 percent above 10 dB. In examining the average backscatter coefficients at large angles, the clutter types cluster according to the following groups: (1) terminals (-3 dB), (2) city and industrial (-7 dB), (3) warehouse (-10 dB), (4) urban and residential (-14 dB), and (5) grass (-24 dB).

Onstott, Robert G.↗