diff --git a/docs/source/sensors/Cassini.rst b/docs/source/sensors/Cassini.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a673b498f859c070da282a7a1b8dbe9235ff4de7
--- /dev/null
+++ b/docs/source/sensors/Cassini.rst
@@ -0,0 +1,39 @@
+Cassini ISS
+===========
+The Cassini Imaging Subsystem comprises two instruments -- a narrow angle camera
+and a wide angle camera. The narrow angle camera contains 24 filters, and the
+wide angle camera contains 18 filters.
+
+Processing Cassini ISS Images
+-----------------------------
+ import ale, json, os
+ from ale.drivers.cassini_drivers import CassiniIssPds3LabelNaifSpiceDriver
+ from ale.drivers import JsonEncoder
+
+ # Use the images to generate ISDs and create CSM cameras
+ # Assume images are in current directory
+
+ nac_stereo_1 = 'N1702360370_1.LBL'
+ nac_stereo_2 = 'N1702360308_1.LBL'
+
+ def generate_isd(filename):
+ driver = CassiniIssPds3LabelNaifSpiceDriver(filename)
+
+ # SPICE kernels are furnished inside this with
+ with driver as d:
+ # this is the information for the ISD in a python dict
+ aledict = d.to_dict()
+
+ # Export python dictionary ISD to external json file to be used by CSM
+ alelabel = os.path.splitext(filename)[0]+".json"
+ with open (alelabel, "w") as file:
+ json.dump(aledict, file, cls=JsonEncoder)
+ return aledict
+
+ # Generate ISD and export to a json file
+ nac1_dict = generate_isd(nac_stereo_1)
+ nac2_dict = generate_isd(nac_stereo_2)
+
+ # Construct a camera
+ camera1 = csm.create_csm(nac_stereo_1)
+ camera2 = csm.create_csm(nac_stereo_2)
diff --git a/docs/source/sensors/Dawn.rst b/docs/source/sensors/Dawn.rst
new file mode 100644
index 0000000000000000000000000000000000000000..51cde4b631acec75140b3627312ada0341070dc4
--- /dev/null
+++ b/docs/source/sensors/Dawn.rst
@@ -0,0 +1,7 @@
+Dawn FC
+=======
+The Dawn Framing Camera is a frame camera with a resolution of up to 25 meters
+per pixel. This instrument contains a clear filter and 7 band-pass filters.
+
+Processing Dawn FC Images
+-------------------------
diff --git a/docs/source/sensors/Galileo.rst b/docs/source/sensors/Galileo.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9de137cb86d3f544b2b232c85527ad37f721d023
--- /dev/null
+++ b/docs/source/sensors/Galileo.rst
@@ -0,0 +1,8 @@
+Galileo SSI
+===========
+The Galileo Solid State Imager is a modified form of the Voyager Narrow Angle
+Camera. Galileo SSI has 8 band filters and is capable of capturing imagery with
+a spatial resolution up to 1 kilometer per pixel.
+
+Processing Galileo SSI Images
+-----------------------------
diff --git a/docs/source/sensors/Hayabusa.rst b/docs/source/sensors/Hayabusa.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d6d8d4bd3d434b2d09ee7a6531ee11cb6fa4ccca
--- /dev/null
+++ b/docs/source/sensors/Hayabusa.rst
@@ -0,0 +1,9 @@
+Hayabusa ONC
+============
+The Hayabusa Optical Navigation Camera is suite of three instruments (two wide
+angle and one telescopic imager), and is primarily used for navigation. The
+wide angle cameras employ clear filters, and the telescopic imager uses 7
+color filters and one clear filter.
+
+Processing Hayabusa ONC Images
+------------------------------
diff --git a/docs/source/sensors/Juno.rst b/docs/source/sensors/Juno.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9a22d2701117c92ef68f032c13c7726ba2f8834c
--- /dev/null
+++ b/docs/source/sensors/Juno.rst
@@ -0,0 +1,8 @@
+JunoCam
+=======
+JunoCam is a push frame camera attached to the Juno spacecraft. This camera
+uses four filters and has an intended spatial resolution between 3 and 15
+kilometers per pixel.
+
+Processing JunoCam Images
+-------------------------
diff --git a/docs/source/sensors/LunarReconnaissanceOrbiter.rst b/docs/source/sensors/LunarReconnaissanceOrbiter.rst
new file mode 100644
index 0000000000000000000000000000000000000000..168e9df215ba0551b76ebc055d163b5ceedf816d
--- /dev/null
+++ b/docs/source/sensors/LunarReconnaissanceOrbiter.rst
@@ -0,0 +1,81 @@
+MiniRF
+======
+MiniRF is the Lunar Reconnaissance Orbiter's Miniature Radio Frequency instrument.
+This instrument features two wavelength bands, and has a resolution of 30 meters
+per pixel.
+
+
+Processing MiniRF Images
+------------------------
+
+LRO NAC
+=======
+LRO NAC is the Lunar Reconnaissance Orbiter's Narrow Angle Camera. It is a
+panchromatic linescan imager with a resolution of .5 meters per pixel over a 5km
+swath.
+
+Processing LRO NAC Images
+-------------------------
+
+LRO WAC
+=======
+LRO WAC is the Lunar Reconnaissance Orbiter's Wide Angle Camera. This is a
+pushframe camera with a resolution of 100 meters per pixel over a 65km swath.
+This instrument captures data in 7 bands.
+
+Processing LRO WAC Images
+-------------------------
+LRO WAC uses a pushframe camera model, and an overview of the processing steps
+for this sensor is as follows:
+
+1. Use ISIS to ingest, calibrate, and spiceinit your cubes
+2. (Optional) photometrically correct the images
+3. Use framestitch to re-combine your even and odd frames
+4. Use ale to generate ISDs for your images, the framestitch application will
+ strip the ISIS camera model info off the cubes, so you'll need to pass ALE
+ your un-stitched images. It doesn't matter if you use even or odd´.
+5. Load your generated ISDs and stitched cubes into your CSM SET
+
+
+This example uses M119929852ME.IMG, which can be easily downloaded from the
+terminal via
+
+ $ curl http://pds.lroc.asu.edu/data/LRO-L-LROC-2-EDR-V1.0/LROLRC_0002/DATA/MAP/2010035/WAC/M119929852ME.IMG -o M119929852ME.IMG
+
+
+Ingesting, calibrating, and spiceinitting cubes
+-----------------------------------------------
+Ingesting, calibrating, and spiceinitting files results in an ISIS´-formatted
+image file that has been dark-field, flat-field, radiometric, and temperature
+corrected and initialized with spice kernels. These processes can be completed
+in a terminal with the following ISIS commands:
+
+ $ lrowac2isis from= M119929852ME.IMG to = M119929852ME.cub
+ $ lrowaccal from= M119929852ME.vis.even.cub to= M119929852ME.vis.even.cal.cub
+ $ lrowaccal from= M119929852ME.vis.odd.cub to= M119929852ME.vis.odd.cal.cub
+ $ spiceinit from= M119929852ME.vis.even.cal.cub
+ $ spiceinit from= M119929852ME.vis.odd.cal.cub
+
+Photometrically correcting cubes (optional)
+-------------------------------------------
+Further research is necessary before ASC developers can recommend specific
+parameters for photometric correction. While the following call is used to
+perform the correction, the determination of the specific values used in the
+input PVL is left to the user.
+
+ $ photomet from= M119929852ME.vis.even.cal.cub to= M119929852ME.vis.even.cal.cor.cub
+ $ photomet from= M119929852ME.vis.odd.cal.cub to= M119929852ME.vis.odd.cal.cor.cub
+
+
+Using framestitch to stitch even and odd frames together
+---------------------------------------------------------
+Framestitch is responsible for combining the even and odd framelets into a single
+image. Note that the resulting, stitched image will not include camera model
+information.
+
+ $ framestitch even= M119929852ME.vis.even.cal.cor.cub odd= M119929852ME.vis.odd.cal.cub to= M119929852ME.vis.stitched.cal.cor.cub
+
+
+Generating ISDs
+---------------
+For information on generating an ISD, review the section on :ref:`isd_generation`.
diff --git a/docs/source/sensors/MarsExpress.rst b/docs/source/sensors/MarsExpress.rst
new file mode 100644
index 0000000000000000000000000000000000000000..96e037f99c066d23017fc9f3473cd02c95ccb342
--- /dev/null
+++ b/docs/source/sensors/MarsExpress.rst
@@ -0,0 +1,21 @@
+MEX HRSC
+========
+* HRSC has 9 different filters. Each has it's own instrument id, as well as
+ the main/"HEAD" camera composing those filters. There is also another
+ "SRC" channel, making-up a total of 11 distinct sensors. It is very
+ important to understand which code is needed when/where.
+
+* HRSC is a variable rate line scanner, and so does not maintain one exposure
+ duration, but rather differing exposure durations per line. This
+ information is stored within the individual records in the image data
+ itself, with the the first 8 bytes making up the double presicion
+ ephemeris time that the line exposure was started, and the next 4 bytes
+ making up the float containing that line's exposure duration.
+
+* The SRC channel is a high resolution framing sensor.
+
+Processing HRSC Images
+----------------------
+
+Processing SRC Images
+---------------------
diff --git a/docs/source/sensors/MarsReconnaissanceOrbiter.rst b/docs/source/sensors/MarsReconnaissanceOrbiter.rst
new file mode 100644
index 0000000000000000000000000000000000000000..41b35096a854197db042aa068483d8c5bc5758ad
--- /dev/null
+++ b/docs/source/sensors/MarsReconnaissanceOrbiter.rst
@@ -0,0 +1,9 @@
+MRO CTX
+=======
+The Mars Reconnaissance Orbiter Context Camera (CTX) is a high resolution linescan
+imager that is capable of capturing grayscale images with a resolution of up to
+6 meters per pixel.
+
+
+Processing MRO CTX Images
+-------------------------
diff --git a/docs/source/sensors/Messenger.rst b/docs/source/sensors/Messenger.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a59f668dd5efaba91fb605a0d83da897223525d5
--- /dev/null
+++ b/docs/source/sensors/Messenger.rst
@@ -0,0 +1,10 @@
+Messenger MDIS
+==============
+The Messenger Mercury Dual Imaging System (MDIS) is a suite of two framing
+cameras-- a wide angle camera (WAC) and a narrow angle camera (NAC). The wide
+angle camera captures multispectral information, and the narrow angle camera
+captures data in monochrome format. The combined cameras result in global mosaics
+with an average spatial resolution of 200 meters per pixel.
+
+Processing MDIS Images
+----------------------
diff --git a/docs/source/sensors/NewHorizons.rst b/docs/source/sensors/NewHorizons.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2a443439fb3d9567d508157f498ac74d1bff40dc
--- /dev/null
+++ b/docs/source/sensors/NewHorizons.rst
@@ -0,0 +1,9 @@
+New Horizons LORRI
+==================
+New Horizons Long Range Reconnaissance Imager is a fixed, narrow angle frame
+camera with a field of view of .29 degrees. LORRI is a panchromatic imaging
+system, and it offers a maximum spatial resolution of roughly 33 meters per
+pixel.
+
+Processing New Horizons LORRI Images
+------------------------------------
diff --git a/docs/source/sensors/Odyssey.rst b/docs/source/sensors/Odyssey.rst
new file mode 100644
index 0000000000000000000000000000000000000000..cf74fd36f3614f37bccbefd8deafb4546de28813
--- /dev/null
+++ b/docs/source/sensors/Odyssey.rst
@@ -0,0 +1,18 @@
+Odyssey THEMIS IR
+=================
+The Thermal Emission Imaging System Infrared sensor is a linescan camera
+responsible for capturing 10 infrared bands at a spatial resolution of 100
+meters per pixel.
+
+
+Processing THEMIS IR Images
+---------------------------
+
+Odyssey THEMIS VIS
+==================
+The Thermal Emission Imaging System Visible sensor is a push frame camera
+responsible for capturing 5 color bands at a spatial resolution of 18 meters
+per pixel.
+
+Processing THEMIS VIS Images
+----------------------------
diff --git a/docs/source/sensors/Selene.rst b/docs/source/sensors/Selene.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9b58192a0c58c6f23f4adb178bf7187bd979c206
--- /dev/null
+++ b/docs/source/sensors/Selene.rst
@@ -0,0 +1,47 @@
+Kaguya TC
+=========
+The Kaguya Terrain Camera is a dual-band, stereo, linescan camera with a spatial
+resolution of 20 meters per pixel. This set of sensors uses two telescopic
+imaging instruments, one pointed forward, and one pointed backward. Together,
+these instruments provide data necessary to create stereoscopic imagery.
+
+Processing Kaguya TC Images
+---------------------------
+Begin by generating an ISD as described in the section on :ref:`isd_generation`.
+
+ import json
+ import os
+ import csmapi
+ import ale
+ import pvl
+ from ale.drivers import JsonEncoder
+ from ale.drivers.kaguya_drivers import KaguyaTcPds3NaifSpiceDriver
+
+ image = "images/TC2W2B0_01_00366S490E1640.img"
+ alelabel = os.path.splitext(image)[0]+".json"
+
+ label = pvl.load(image)
+ driver = KaguyaTcPds3NaifSpiceDriver(image)
+
+ with driver as d:
+ aledict = d.to_dict()
+
+ json.dump(aledict, open(alelabel, "w"), cls=JsonEncoder)
+
+ nlines, nsamples = aledict["image_lines"], aledict["image_samples"]
+
+ model="USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL"
+ plugin = csmapi.Plugin.getList()[0]
+ isd = csmapi.Isd(alelabel)
+ camera = plugin.constructModelFromISD(isd, model)
+
+Kaguya MI
+=========
+The Kaguya Multiband Imagery platform comprises two different instruments -- a
+visible light sensor (VIS) and a near infrared sensor (NIR). The visible light
+sensor has a spatial resolution of 20 meters per pixel, and collects 5 bands of
+data via a bandpass filter. The near infrared sensor is responsible for
+collecting 4 bands of data at a resolution of 62 meters per pixel.
+
+Processing Kaguya MI Images
+---------------------------
diff --git a/docs/source/sensors/TraceGasOrbiter.rst b/docs/source/sensors/TraceGasOrbiter.rst
new file mode 100644
index 0000000000000000000000000000000000000000..147fb5b2925d23aa3285ca68f4708b5e3d4ea325
--- /dev/null
+++ b/docs/source/sensors/TraceGasOrbiter.rst
@@ -0,0 +1,9 @@
+TGO CaSSIS
+==========
+The Trace Gas Orbiter's Color and Stereo Surface Imaging System is a 4 filter
+frame camera capable of capturing data at a resolution of 4.6 meters per pixel.
+The camera is mounted on a rotating mechanism, and it is able to capture stereo
+imagery in a single pass.
+
+Processing TGO CaSSIS Images
+----------------------------
diff --git a/docs/source/sensors/Viking.rst b/docs/source/sensors/Viking.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f0c400321d9d38683ab5eab3c3110b2943251987
--- /dev/null
+++ b/docs/source/sensors/Viking.rst
@@ -0,0 +1,8 @@
+Viking
+======
+The Viking visual imaging subsystem comprises two high-resolution framing
+cameras. These cameras are identical, and each camera contains 6 color imaging
+filters and one clear filter.
+
+Processing Viking Images
+------------------------
diff --git a/docs/source/sensors/Voyager.rst b/docs/source/sensors/Voyager.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3407df33d909e80bfe07298d5dc14b12872188af
--- /dev/null
+++ b/docs/source/sensors/Voyager.rst
@@ -0,0 +1,9 @@
+Voyager
+=======
+The voyager imaging system is a set of two instruments -- a wide angle camera
+(WAC) and a narrow angle camera (NAC). The wide angle camera contains 8 filters
+and has a field of view of 3.2 degrees. The narrow angle camera also contains 8
+filters, and has a field of view of .4 degrees.
+
+Processing Voyager Images
+-------------------------
diff --git a/docs/source/user/creating_an_environment.rst b/docs/source/user/creating_an_environment.rst
new file mode 100644
index 0000000000000000000000000000000000000000..132a5e55b41c072cc0f6c3e0f18fa29131c2e105
--- /dev/null
+++ b/docs/source/user/creating_an_environment.rst
@@ -0,0 +1,11 @@
+Creating an environment
+-----------------------
+Due to version incompatibilities, setting up a full environment can be somewhat
+difficult to navigate, and it is recommended that you follow these steps. This
+guide assumes that you already have anaconda set up on your computer. If you do
+not, please follow `these instructions <https://www.anaconda.com/products/distribution>`_.
+
+ conda create -n csm python=3.6
+ conda activate csm
+ conda install -c conda-forge csmapi
+ conda env update -n csm -f environment.yml
diff --git a/docs/source/user/isd_generation.rst b/docs/source/user/isd_generation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..55f8b75c06871dc9953d57edbd8339c529839134
--- /dev/null
+++ b/docs/source/user/isd_generation.rst
@@ -0,0 +1,69 @@
+.. _isd_generation:
+
+Creating and testing an ISD
+===
+
+This tutorial assumes that the user has created a conda environment with ale, usgscsm,
+and csmapi installed.
+
+Generating an ISD with ALE
+-----------------
+ISD generation is the first step in processing an image with the models provided
+by USGSCSM. ISDs can be easily generated with the use of
+`ALE <https://github.com/USGS-Astrogeology/ale>`_, which generates ISDs through
+the use of the "load" and "loads" functions After installing the library
+(as described on the repository), one can generate an ISD using the image's label
+in the following fashion::
+
+ import ale
+
+ image_label_path = "/path/to/my/image.lbl"
+ isd_string = ale.loads(image_label_path)
+
+Under some circumstances, ALE fails to identify a set of kernels to properly
+generate an ISD, and users will receive the message
+
+ "No Such Driver for Label."
+
+Under these circumstances, the user must explicitly pass the desired kernel set
+to the loads function. This kernel set can be generated from a spiceinit'd cub
+and passed to ale as follows:
+
+ kernels = ale.util.generate_kernels_from_cube(spice_initted_cub, expand=True)
+ isd = ale.loads(img_file, props={'kernels': kernels})
+
+After generating the ISD in Python, it is necessary to save it to disc. This is
+accomplished via Python builtin functions such as:
+
+ with open('out.json', 'w') as f:
+ f.write(isd)
+
+Generating an ISD with Pfeffernusse
+-------------
+Alternatively, ISDs can be generated via web service through the API exposed by
+`Pfeffernusse <https://app.swaggerhub.com/apis/USGS-Astro/pfeffernusse2/0.1.4-oas3>`_.
+While the web service is not yet publicly hosted, a public version of the service
+is under development. Individuals or organizations may choose to host their own
+service, and it can be accessed as follows::
+
+ curl -X POST "http://<servername>:<port>/v1/pds/" -H \
+ "accept: application/json" -H "Content-Type: application/json" \
+ -d @EN1040199536M_tmp.json
+
+where EN1040199536M_tmp.json is a file containing a JSON representation of an
+image label.
+
+Testing the ISD
+---------------
+USGSCSM includes a test utility that can be used to verify the integrity of an
+ISD by loading a camera model and performing basic tests. This utility is
+located in the USGSCSM build directory, and requires only the path to the ISD
+from the previous step.
+
+ ./usgscsm_cam_test --model /Path/to/isd.json
+
+A successful test will result in output similar to the following:
+
+ Detected CSM plugin: UsgsAstroPluginCSM
+ Number of models for this plugin: 4
+ Loaded a CSM model of type USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL from ISD file ...