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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

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Base 60 encoding of positive floating point numbers in IDL

Anonym

Here is an example of representing numbers efficiently using a restricted set of symbols. I am using a set of 60 symbols (or characters) to encode floating point numbers as strings of any selected length. The longer the strings are, the more precise the numbers will potentially be.

 

Here is an example of a representation, this is restricted to positive numbers, in order to keep the example short.

 
IDL> a=[14.33, 3.1415, 12345]
IDL> a
       14.330000       3.1415000       12345.000
IDL> base60(a)
FotV*
FdiDx
HdzS*
IDL> base60(a, precision=8)
FotV**aO
FdiDx*^c
HdzS****
IDL> base60(base60(a)) - a
 -4.5533356836102712e-006 -4.6258149324351905e-006    -0.016666666666424135
IDL> base60(base60(a, precision=8)) - a
 -9.2104102122902987e-012 -4.6052051061451493e-013 -7.7159711509011686e-008
 
In this example, it can be seen that the 5-digit representations are not as close to the original numbers as the 8-digit representations.
 
The code example for the base60 function is listed below.
;
; Converts from a numeric type to a base 60 representation
; Converts from a base 60 string to a floating point representation
; PRECISION is only used to determine how many symbols to use when encoding,
; and is ignored for decoding.
function Base60, input, precision=precision
  compile_opt idl2,logical_predicate
 
  ; set default precision of 5 digits for encoding only
  if ~keyword_set(precision) then precision = 5
 
  ; base 60 symbology
  symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*'
  base = strlen(symbols)
 
  ; fast conversion from symbol to value
  lut = bytarr(256)
  lut[byte(symbols)] = bindgen(base)
 
  if isa(input, /string) then begin
    ; convert from base60 string to float
    ; find exponent first
    scale = replicate(double(base),n_elements(input)) ^ $
      (lut[byte(strmid(input,0,1))] - base/2)
    res = dblarr(n_elements(input))
    for i=max(strlen(input))-1,1,-1 do begin
      dig = lut[byte(strmid(input,i,1))]
      res += dig
      res /= base
    endfor
    res *= scale
  endif else begin
    ; convert from float to base60 strings
    ; encode exponent(scale) first
    ex = intarr(n_elements(input))
    arr = input
    dbase = double(base)
    repeat begin
      dec = fix(arr ge 1)
      ex += dec
      arr *= dbase ^ (-dec)
      inc = fix(arr lt 1/dbase)
      ex -= inc
      arr *= dbase ^ inc
    endrep until array_equal(arr lt 1 and arr ge 1/dbase,1b)
    if max(ex) ge base/2 || min(ex) lt -base/2 then begin
      message, 'Number is outside representable range'
    endif
    bsym = byte(symbols)
    res = string(bsym[reform(ex+base/2,1,n_elements(ex))])
    for i=1,precision-1 do begin
      arr *= base
      fl = floor(arr)
      arr -= fl
      res += string(bsym[reform(fl,1,n_elements(fl))])
    endfor
  endelse
  return, res
end
 
 
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